The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
State–space models of individual animal movement
Open accessAbstract
Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.
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Cites
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Electronic tagging and population structure of Atlantic bluefin tuna
Block, B. A., Teo, S. L. H., Walli, A., Boustany, A., Stokesbury, M. J. W., Farwell, C. J., Weng, K. C., Dewar, H., & Williams, T. D. (2005) · Nature · doi:10.1038/nature03463 · Cited by 781 · cited 1×
“Advances in technologies such as satellite and Global Positioning System (GPS) transmitters [13], harmonic radar tracking [14] and electronic data storage tags [ 15 ] have led to a vast influx of data on the movement of tagged individuals [13] from a diverse range of species in habitats where direct observation is oft…”
BackgroundClark, J. S., & Bjørnstad, O. N. (2004) · Ecology · doi:10.1890/03-0520 · Cited by 348
Cited by
Clobert, J., Le Galliard, J., Cote, J., Meylan, S., & Massot, M. (2009) · Ecology Letters · doi:10.1111/j.1461-0248.2008.01267.x · Cited by 1,232
Integrated step selection analysis: bridging the gap between resource selection and animal movement
Avgar, T., Potts, J. R., Lewis, M. A., & Boyce, M. S. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12528 · Cited by 594
Frair, J. L., Fieberg, J., Hebblewhite, M., Cagnacci, F., DeCesare, N. J., & Pedrotti, L. (2010) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2010.0084 · Cited by 420
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
A Disease-Mediated Trophic Cascade in the Serengeti and its Implications for Ecosystem C
Holdo, R. M., Sinclair, A. R. E., Dobson, A. P., Metzger, K. L., Bolker, B. M., Ritchie, M. E., & Holt, R. D. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000210 · Cited by 298
“A technique that is increasingly gaining currency in ecological studies for the analysis of time series data with nonlinear dynamics, process and observation error, missing data, and latent variables is the BSS model using Gibbs sampling [49–52].”
Method
Metadata
- Authors
- PATTERSON, T., THOMAS, L., WILCOX, C., OVASKAINEN, O., & MATTHIOPOULOS, J.
- Venue
- Trends in Ecology & Evolution
- Publisher
- Elsevier BV
- Published
- 2008-02
- Volume
- 23
- Issue
- 2
- Pages
- 87-94
- ISSN
- 0169-5347 (ISSN-L)
- OpenAlex
- W2057314736
- Cited by
- 921
- References
- 3
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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- PATTERSON, T., THOMAS, L., WILCOX, C., OVASKAINEN, O., & MATTHIOPOULOS, J. (2008). State–space models of individual animal movement. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2007.10.009
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
