Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions

Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM (2012)
Ecology 93(11): 2336-2342.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Langrock, RolandUniBi; King, Ruth; Matthiopoulos, Jason; Thomas, Len; Fortin, Daniel; Morales, Juan M.
Erscheinungsjahr
2012
Zeitschriftentitel
Ecology
Band
93
Ausgabe
11
Seite(n)
2336-2342
ISSN
0012-9658
Page URI
https://pub.uni-bielefeld.de/record/2902621

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Langrock R, King R, Matthiopoulos J, Thomas L, Fortin D, Morales JM. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology. 2012;93(11):2336-2342.
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., & Morales, J. M. (2012). Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93(11), 2336-2342. doi:10.1890/11-2241.1
Langrock, Roland, King, Ruth, Matthiopoulos, Jason, Thomas, Len, Fortin, Daniel, and Morales, Juan M. 2012. “Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions”. Ecology 93 (11): 2336-2342.
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., and Morales, J. M. (2012). Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology 93, 2336-2342.
Langrock, R., et al., 2012. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93(11), p 2336-2342.
R. Langrock, et al., “Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions”, Ecology, vol. 93, 2012, pp. 2336-2342.
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., Morales, J.M.: Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology. 93, 2336-2342 (2012).
Langrock, Roland, King, Ruth, Matthiopoulos, Jason, Thomas, Len, Fortin, Daniel, and Morales, Juan M. “Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions”. Ecology 93.11 (2012): 2336-2342.

41 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity - a multivariate hidden Markov model.
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A quantitative, hierarchical approach for detecting drift dives and tracking buoyancy changes in southern elephant seals.
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From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest.
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Papastamatiou YP, Watanabe YY, Demšar U, Leos-Barajas V, Bradley D, Langrock R, Weng K, Lowe CG, Friedlander AM, Caselle JE., Mov Ecol 6(), 2018
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Scaling marine fish movement behavior from individuals to populations.
Griffiths CA, Patterson TA, Blanchard JL, Righton DA, Wright SR, Pitchford JW, Blackwell PG., Ecol Evol 8(14), 2018
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An analysis of pilot whale vocalization activity using hidden Markov models.
Popov V, Langrock R, DeRuiter SL, Visser F., J Acoust Soc Am 141(1), 2017
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A hidden Markov movement model for rapidly identifying behavioral states from animal tracks.
Whoriskey K, Auger-Méthé M, Albertsen CM, Whoriskey FG, Binder TR, Krueger CC, Mills Flemming J., Ecol Evol 7(7), 2017
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Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.
Quick NJ, Isojunno S, Sadykova D, Bowers M, Nowacek DP, Read AJ., Sci Rep 7(), 2017
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Estimation and simulation of foraging trips in land-based marine predators.
Michelot T, Langrock R, Bestley S, Jonsen ID, Photopoulou T, Patterson TA., Ecology 98(7), 2017
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Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns.
Heerah K, Woillez M, Fablet R, Garren F, Martin S, De Pontual H., Mov Ecol 5(), 2017
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Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior.
Chimienti M, Cornulier T, Owen E, Bolton M, Davies IM, Travis JMJ, Scott BE., Ecol Evol 7(23), 2017
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Quantifying animal movement for caching foragers: the path identification index (PII) and cougars, Puma concolor.
Ironside KE, Mattson DJ, Theimer T, Jansen B, Holton B, Arundel T, Peters M, Sexton JO, Edwards TC., Mov Ecol 5(), 2017
PMID: 29201376
What is the animal doing? Tools for exploring behavioural structure in animal movements.
Gurarie E, Bracis C, Delgado M, Meckley TD, Kojola I, Wagner CM., J Anim Ecol 85(1), 2016
PMID: 25907267
Semi-Markov Arnason-Schwarz models.
King R, Langrock R., Biometrics 72(2), 2016
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The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data.
Chimienti M, Cornulier T, Owen E, Bolton M, Davies IM, Travis JM, Scott BE., Ecol Evol 6(3), 2016
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Expectation-Maximization Binary Clustering for Behavioural Annotation.
Garriga J, Palmer JR, Oltra A, Bartumeus F., PLoS One 11(3), 2016
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Evaluating random search strategies in three mammals from distinct feeding guilds.
Auger-Méthé M, Derocher AE, DeMars CA, Plank MJ, Codling EA, Lewis MA., J Anim Ecol 85(5), 2016
PMID: 27354185
Putting the behavior into animal movement modeling: Improved activity budgets from use of ancillary tag information.
Bestley S, Jonsen I, Harcourt RG, Hindell MA, Gales NJ., Ecol Evol 6(22), 2016
PMID: 27878092
Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds.
Carter MI, Bennett KA, Embling CB, Hosegood PJ, Russell DJ., Mov Ecol 4(), 2016
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Hidden semi-Markov models reveal multiphasic movement of the endangered Florida panther.
van de Kerk M, Onorato DP, Criffield MA, Bolker BM, Augustine BC, McKinley SA, Oli MK., J Anim Ecol 84(2), 2015
PMID: 25251870
Nonparametric inference in hidden Markov models using P-splines.
Langrock R, Kneib T, Sohn A, DeRuiter SL., Biometrics 71(2), 2015
PMID: 25586063
Diving deeper into individual foraging specializations of a large marine predator, the southern sea lion.
Baylis AM, Orben RA, Arnould JP, Peters K, Knox T, Costa DP, Staniland IJ., Oecologia 179(4), 2015
PMID: 26323982
Of scales and stationarity in animal movements.
Benhamou S., Ecol Lett 17(3), 2014
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Modeling the Diving Behavior of Whales: A Latent-Variable Approach with Feedback and Semi-Markovian Components
Langrock R, Marques TA, Baird RW, Thomas L., J Agric Biol Environ Stat 19(1), 2014
PMID: IND500735807
When to be discrete: the importance of time formulation in understanding animal movement.
McClintock BT, Johnson DS, Hooten MB, Ver Hoef JM, Morales JM., Mov Ecol 2(1), 2014
PMID: 25709830
Hidden Markov models: the best models for forager movements?
Joo R, Bertrand S, Tam J, Fablet R., PLoS One 8(8), 2013
PMID: 24058400
A Bayesian Approach to Fitting Gibbs Processes with Temporal Random Effects
King R, Illian JB, King SE, Nightingale GF, Hendrichsen DK., J Agric Biol Environ Stat 17(4), 2012
PMID: IND500615197

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