The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS
Abstract
We present a framework for fitting multiple random walks to animal movement paths consisting of ordered sets of step lengths and turning angles. Each step and turn is assigned to one of a number of random walks, each characteristic of a different behavioral state. Behavioral state assignments may be inferred purely from movement data or may include the habitat type in which the animals are located. Switching between different behavioral states may be modeled explicitly using a state transition matrix estimated directly from data, or switching probabilities may take into account the proximity of animals to landscape features. Model fitting is undertaken within a Bayesian framework using the WinBUGS software. These methods allow for identification of different movement states using several properties of observed paths and lead naturally to the formulation of movement models. Analysis of relocation data from elk released in east-central Ontario, Canada, suggests a biphasic movement behavior: elk are either in an “encamped” state in which step lengths are small and turning angles are high, or in an “exploratory” state, in which daily step lengths are several kilometers and turning angles are small. Animals encamp in open habitat (agricultural fields and opened forest), but the exploratory state is not associated with any particular habitat type.
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Cites
General methods for monitoring convergence of iterative simulations
Brooks, S. P., & Gelman, A. (1998) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1998.10474787 · Cited by 6,045
Cited by
Ecological models and data in R
Bolker, B. M. (2008) · doi:10.2307/j.ctvcm4g37 · Cited by 1,297
“It has already begun to appear in ecological journals (Jonsen et al., 2003; Morales et al., 2004; McCarthy and Parris, 2004; Okuyama and Bolker, 2005; Parris, 2006; Vesk, 2006), but statisticians continue to debate its exact meaning and appropriateness (both Spiegelhalter et al.”
BackgroundBörger, L., Dalziel, B. D., & Fryxell, J. M. (2008) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01182.x · Cited by 616
Self-citationMueller, T., & Fagan, W. F. (2008) · Oikos · doi:10.1111/j.0030-1299.2008.16291.x · Cited by 399
Optimizing the use of biologgers for movement ecology research
Williams, H. J., Taylor, L. A., Benhamou, S., Bijleveld, A. I., Clay, T. A., de Grissac, S., Demšar, U., English, H. M., Franconi, N., Gómez‐Laich, A., et al. (2020) · Journal of Animal Ecology · doi:10.1111/1365-2656.13094 · Cited by 333
Self-citationPohle, J., Langrock, R., van Beest, F. M., & Schmidt, N. M. (2017) · Journal of Agricultural, Biological and Environmental Statistics · doi:10.1007/s13253-017-0283-8 · Cited by 288
Stopover ecology of a migratory ungulate: Ungulate stopover ecology
Sawyer, H., & Kauffman, M. J. (2011) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2011.01845.x · Cited by 240
Northrup, J. M., Vander Wal, E., Bonar, M., Fieberg, J., Laforge, M. P., Leclerc, M., Prokopenko, C. M., & Gerber, B. D. (2022) · Ecological Applications · doi:10.1002/eap.2470 · Cited by 227
Animal behavior, cost-based corridor models, and real corridors
LaPoint, S., Gallery, P., Wikelski, M., & Kays, R. (2013) · Landscape Ecology · doi:10.1007/s10980-013-9910-0 · Cited by 203
Machine learning for inferring animal behavior from location and movement data
Wang, G. (2019) · Ecological Informatics · doi:10.1016/j.ecoinf.2018.12.002 · Cited by 154
Analysis and visualisation of movement: an interdisciplinary review
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., & Weibel, R. (2015) · Movement Ecology · doi:10.1186/s40462-015-0032-y · Cited by 128
Zeller, K. A., McGarigal, K., Beier, P., Cushman, S. A., Vickers, T. W., & Boyce, W. M. (2014) · Landscape Ecology · doi:10.1007/s10980-014-9991-4 · Cited by 123
Suite of simple metrics reveals common movement syndromes across vertebrate taxa
Abrahms, B., Seidel, D. P., Dougherty, E., Hazen, E. L., Bograd, S. J., Wilson, A. M., Weldon McNutt, J., Costa, D. P., Blake, S., Brashares, J. S., et al. (2017) · Movement Ecology · doi:10.1186/s40462-017-0104-2 · Cited by 109
Wall, J., Wittemyer, G., Klinkenberg, B., LeMay, V., & Douglas-Hamilton, I. (2013) · Biological Conservation · doi:10.1016/j.biocon.2012.07.019 · Cited by 96
Applying network theory to animal movements to identify properties of landscape space use
Bastille‐Rousseau, G., Douglas‐Hamilton, I., Blake, S., Northrup, J. M., & Wittemyer, G. (2018) · Ecological Applications · doi:10.1002/eap.1697 · Cited by 51
Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A., & Dennis, T. E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122811 · Cited by 44
Maiorano, L., Boitani, L., Chiaverini, L., & Ciucci, P. (2017) · Basic and Applied Ecology · doi:10.1016/j.baae.2017.02.005 · Cited by 30
Metadata
- Authors
- Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., & Fryxell, J. M.
- Venue
- Ecology
- Publisher
- Wiley
- Published
- 2004-09
- Volume
- 85
- Issue
- 9
- Pages
- 2436-2445
- ISSN
- 0012-9658 (ISSN-L)
- OpenAlex
- W2066418236
- Cited by
- 799
- References
- 1
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- closed
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