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
 
Download
Es wurde kein Volltext hochgeladen. Nur Publikationsnachweis!
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

Zitieren

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, 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.
van Beest FM, Mews S, Elkenkamp S, Schuhmann P, Tsolak D, Wobbe T, Bartolino V, Bastardie F, Dietz R, von Dorrien C, Galatius A, Karlsson O, McConnell B, Nabe-Nielsen J, Olsen MT, Teilmann J, Langrock R., Sci Rep 9(1), 2019
PMID: 30948786
A quantitative, hierarchical approach for detecting drift dives and tracking buoyancy changes in southern elephant seals.
Arce F, Bestley S, Hindell MA, McMahon CR, Wotherspoon S., Sci Rep 9(1), 2019
PMID: 31222003
Search and foraging behaviors from movement data: A comparison of methods.
Bennison A, Bearhop S, Bodey TW, Votier SC, Grecian WJ, Wakefield ED, Hamer KC, Jessopp M., Ecol Evol 8(1), 2018
PMID: 29321847
From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest.
Torney CJ, Hopcraft JGC, Morrison TA, Couzin ID, Levin SA., Philos Trans R Soc Lond B Biol Sci 373(1746), 2018
PMID: 29581397
Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming.
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
PMID: 29951206
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
PMID: 30073065
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
PMID: 28147612
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
PMID: 28405277
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
PMID: 28361954
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
PMID: 28470722
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
PMID: 28944062
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
PMID: 29238552
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
PMID: 26584064
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
PMID: 26865961
Expectation-Maximization Binary Clustering for Behavioural Annotation.
Garriga J, Palmer JR, Oltra A, Bartumeus F., PLoS One 11(3), 2016
PMID: 27002631
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
PMID: 27800161
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
PMID: 24350897
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

21 References

Daten bereitgestellt von Europe PubMed Central.


AUTHOR UNKNOWN, 0
Energy gains predict the distribution of plains bison across populations and ecosystems.
Babin JS, Fortin D, Wilmshurst JF, Fortin ME., Ecology 92(1), 2011
PMID: 21560694

AUTHOR UNKNOWN, 0
Random walk models in biology.
Codling EA, Plank MJ, Benhamou S., J R Soc Interface 5(25), 2008
PMID: 18426776
Modeling loggerhead turtle movement in the Mediterranean: importance of body size and oceanography.
Eckert SA, Moore JE, Dunn DC, van Buiten RS, Eckert KL, Halpin PN., Ecol Appl 18(2), 2008
PMID: 18488597

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Continuous-time correlated random walk model for animal telemetry data.
Johnson DS, London JM, Lea MA, Durban JW., Ecology 89(5), 2008
PMID: 18543615

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Extracting more out of relocation data: building movement models as mixtures of random walks
Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM., Ecology 85(9), 2004
PMID: IND604780496
Classifying movement behaviour in relation to environmental conditions using hidden Markov models.
Patterson TA, Basson M, Bravington MV, Gunn JS., J Anim Ecol 78(6), 2009
PMID: 19563470
State-space models of individual animal movement.
Patterson TA, Thomas L, Wilcox C, Ovaskainen O, Matthiopoulos J., Trends Ecol. Evol. (Amst.) 23(2), 2008
PMID: 18191283

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
On the application of mixed hidden Markov models to multiple behavioural time series.
Schliehe-Diecks S, Kappeler PM, Langrock R., Interface Focus 2(2), 2012
PMID: 23565332
Modeling time series of animal behavior by means of a latent-state model with feedback.
Zucchini W, Raubenheimer D, MacDonald IL., Biometrics 64(3), 2007
PMID: 18047533

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 23236905
PubMed | Europe PMC

Suchen in

Google Scholar