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Movement ecology of human resource users: using net squared displacement, biased random bridges and resource utilization functions to quantify hunter and gatherer behaviour
Article first published online: 28 FEB 2012
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Methods in Ecology and Evolution
Volume 3, Issue 3, pages 584–594, June 2012
How to Cite
Papworth, S. K., Bunnefeld, N., Slocombe, K. and Milner-Gulland, E. J. (2012), Movement ecology of human resource users: using net squared displacement, biased random bridges and resource utilization functions to quantify hunter and gatherer behaviour. Methods in Ecology and Evolution, 3: 584–594. doi: 10.1111/j.2041-210X.2012.00189.x
- Issue published online: 7 JUN 2012
- Article first published online: 28 FEB 2012
- Received 13 September 2011; accepted 17 January 2012 Handling Editor: Robert Freckleton
Vol. 5, Issue 2, 200, Article first published online: 13 FEB 2014
- habitats – conservation;
- spatial or time-series – statistics
1. Understanding human resource extraction is crucial for conservation science, allowing accurate assessments of system sustainability and testing key assumptions about human resource users.
2. We apply ecological methods and principles to Global Positioning System (GPS) data on human movement to investigate the ecological and behavioural differences between human hunters and non-hunters, a method which can be reproduced with any species which routinely return to a central place. The integration of movement ecology and habitat selection can greatly augment the applicability and scope of both disciplines, and we explore the issues that arise from integration, because of the differing data types and methods used by each approach.
3. We propose an adaptable methodological framework which can be used to combine movement ecology and habitat selection when using data from GPS tracking devices, whether from human or animal foragers. The methodology is based on three steps and can be implemented in the free downloadable statistical program R, using the full code in the Data S1.
4. First, we show that net squared displacement, coupled with nonlinear mixed-effects models, is suitable for quantifying characteristics of small-scale movement, such as daily travel patterns, and extracting parts of these journeys for analysis.
5. Secondly, having extracted part of a journey, biased random bridges use the auto-correlated GIS tracking data to create a utilization distribution (UD) for each individual. This method includes movement between known locations to estimate use intensity in an area.
6. Finally, the UD can be analysed by a resource utilization function which relates the intensity of use to landscape features of an area to identify habitats selected by an individual. This can be used to predict use of the landscape at larger scales for both individuals and an entire population.
7. This methodological framework is a flexible method to accurately assess human and animal resource use and movement through the natural environment.