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.
Original language | English |
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Article number | 18 |
Journal | Movement Ecology |
Volume | 3 |
Issue number | 1 |
DOIs | |
State | Published - 2015 |
Bibliographical note
Funding Information:Research was supported by COST (European Cooperation in Science and Technology) ICT Action IC0903 MOVE, the Swiss National Science Foundation (Sinergia Grant CRSI33_133040 to Redouan Bshary, Carel van Schaik and Andy Whiten), the Forschungskredit of the University of Zurich (EPW), the Claraz Foundation (EPW) and the Netherlands Organisation for Scientific Research (NWO) under grant no. 612.001.207 (KB). NS was funded by the US-Israel Binational Science Foundation, the Ring Foundation for Environmental Research and the Robert Szold Fund. This work was initiated at Schloss Dagstuhl Seminars on Representation, Analysis and Visualization of Moving Objects (10491, 12512), held in Wadern, Germany. We would like to thank Orr Spiegel, Kamran Safi and Ran Nathan for helpful discussions. Further, we would like to thank Ran Nathan for helping to set up the collaboration and for encouraging us to submit this work.
Funding Information:
Research was supported by COST (European Cooperation in Science and Technology) ICT Action IC0903 MOVE, the Swiss National Science Foundation (Sinergia Grant CRSI33_133040 to Redouan Bshary, Carel van Schaik and Andy Whiten), the Forschungskredit of the University of Zurich (EPW), the Claraz Foundation (EPW) and the Netherlands Organisation for Scientific Research (NWO) under grant no. 612.001.207 (KB). NS was funded by the US – Israel Binational Science Foundation, the Ring Foundation for Environmental Research and the Robert Szold Fund. This work was initiated at Schloss Dagstuhl Seminars on Representation, Analysis and Visualization of Moving Objects (10491, 12512), held in Wadern, Germany. We would like to thank Orr Spiegel, Kamran Safi and Ran Nathan for helpful discussions. Further, we would like to thank Ran Nathan for helping to set up the collaboration and for encouraging us to submit this work.
Publisher Copyright:
© 2015 Buchin et al.
Keywords
- Brownian bridge movement model
- Home range utilization
- Migratory flight behaviour
- Movement speed
- Spatial distribution
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics