title: Deriving movement properties and the effect of the environment from the Brownian bridge movement model in monkeys and birds.
abstract: BACKGROUND
  The Brownian bridge movement model (BBMM) provides a biologically sound approximation of the movement path of an animal based on discrete location data, and is a powerful method to quantify utilization distributions. Computing the utilization distribution based on the BBMM while calculating movement parameters directly from the location data, may result in inconsistent and misleading results. We show how the BBMM can be extended to also calculate derived movement parameters. Furthermore we demonstrate how to integrate environmental context into a BBMM-based analysis.
  
  
  RESULTS
  We develop a computational framework to analyze animal movement based on the BBMM. In particular, we demonstrate how a derived movement parameter (relative speed) and its spatial distribution can be calculated in the BBMM. We show how to integrate our framework with the conceptual framework of the movement ecology paradigm in two related but acutely different ways, focusing on the influence that the environment has on animal movement. First, we demonstrate an a posteriori approach, in which the spatial distribution of average relative movement speed as obtained from a "contextually naïve" model is related to the local vegetation structure within the monthly ranging area of a group of wild vervet monkeys. Without a model like the BBMM it would not be possible to estimate such a spatial distribution of a parameter in a sound way. Second, we introduce an a priori approach in which atmospheric information is used to calculate a crucial parameter of the BBMM to investigate flight properties of migrating bee-eaters. This analysis shows significant differences in the characteristics of flight modes, which would have not been detected without using the BBMM.
  
  
  CONCLUSIONS
  Our algorithm is the first of its kind to allow BBMM-based computation of movement parameters beyond the utilization distribution, and we present two case studies that demonstrate two fundamentally different ways in which our algorithm can be applied to estimate the spatial distribution of average relative movement speed, while interpreting it in a biologically meaningful manner, across a wide range of environmental scenarios and ecological contexts. Therefore movement parameters derived from the BBMM can provide a powerful method for movement ecology research.
authors.affiliation: Department of Mathematics and Computer Science, Technical University Eindhoven, Eindhoven, The Netherlands.
authors.forename: Kevin
authors.initials: K
authors.lastname: Buchin
authors.affiliation: Faculty of Mathematics, Ruhr-Universität Bochum, Bochum, Germany.
authors.forename: Stef
authors.initials: S
authors.lastname: Sijben
authors.affiliation: Computational Geo-Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: E Emiel
authors.initials: EE
authors.lastname: van Loon
authors.affiliation: Department of Evolutionary and Environmental Biology, The University of Haifa, Haifa, Israel.
authors.forename: Nir
authors.initials: N
authors.lastname: Sapir
authors.affiliation: Institut de Biologie, Université de Neuchâtel, Neuchâtel, Switzerland.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Mercier
authors.affiliation: Anthropological Institute & Museum, University of Zurich, Zurich, Switzerland.
authors.forename: T Jean
authors.initials: TJ
authors.lastname: Marie Arseneau
authors.affiliation: Anthropological Institute & Museum, University of Zurich, Zurich, Switzerland.
authors.forename: Erik P
authors.initials: EP
authors.lastname: Willems
keywords: Brownian bridge movement model; Home range utilization; Migratory flight behaviour; Movement speed; Spatial distribution