Skip to main content
SpringerLink
Log in

Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

  • Original Paper
  • Published:
AStA Advances in Statistical Analysis Aims and scope Submit manuscript

Abstract

With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry among biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyse individual animal movement data. Specifically, we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology towards what are arguably overly complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based on mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Lévy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Albertsen, C.M., Whoriskey, K., Yurkowski, D., Nielsen, A., Mills, J.: Fast fitting of non-gaussian state-space models to animal movement data via template model builder. Ecology 96, 2598–2604 (2015). doi:10.1890/14-2101.1

    Article  Google Scholar 

  • Anderson, T., Stephens, M.: The continuous and discrete Brownian bridges: representations and applications. Technical report, Department of Statistics, Stanford University, Stanford, California (1996)

  • Andersen, K., Nielsen, A., Thygesen, U., Hinrichsen, H.-H., Neuenfeldt, S.: Using the particle filter to geolocate atlantic cod (gadus morhua) in the baltic sea, with special emphasis on determining uncertainty. Can. J. Fish. Aquat. Sci. 64, 618–627 (2007)

    Article  Google Scholar 

  • Andrieu, C., Doucet, A., Holenstein, R.: Particle Markov chain Monte Carlo (with discussion). J. R. Stat. Soc. B 62, 269–342 (2010)

    Article  MATH  Google Scholar 

  • Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Process. 10, 174–188 (2002)

    Article  Google Scholar 

  • Bidder, O., Arandjelović, O., Almutairi, F., Shepard, E., Lambertucci, S., Qasem, L., Wilson, R.: A risky business or a safe bet? A fuzzy set event tree for estimating hazard in biotelemetry studies. Anim. Behav. 93, 143–150 (2014)

    Article  Google Scholar 

  • Blackwell, P.G.: Random diffusion models for animal movement. Ecol. Model. 100(1–3), 87–102 (1997). doi:10.1016/S0304-3800(97)00153-1

    Article  Google Scholar 

  • Blackwell, P.G.: Bayesian inference for Markov processes with diffusion and discrete components. Biometrika 90(3), 613–627 (2003). doi:10.1093/biomet/90.3.613

    Article  MathSciNet  MATH  Google Scholar 

  • Blackwell, P.G., Niu, N., Lambert, C., LaPoint, S.: Exact Bayesian inference for animal movement in continuous time. Methods Ecol. Evolut. (2015). doi:10.1111/2041-210X.12460

    Google Scholar 

  • Börger, L., Dalziel, B., Fryxell, J.: Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol. Lett. 11(6), 637–650 (2008). doi:10.1111/j.1461-0248.2008.01182.x

    Article  Google Scholar 

  • Breed, G.A., Costa, D.P., Goebel, M.E., Robinson, P.W.: Electronic tracking tag programming is critical to data collection for behavioral time-series analysis. Ecosphere 2(1), art10 (2011)

    Article  Google Scholar 

  • Brillinger, D., Stewart, B.: Elephant-seal movements: modelling migration. Can. J. Stat. 26(3), 431–443 (1998). doi:10.2307/3315767

    Article  MATH  Google Scholar 

  • Brillinger, D., Preisler, H., Ager, A., Kie, J., Stewart, B.: Employing stochastic differential equations to model wildlife motion. Bull. Braz. Math. Soc. 33(3), 385–408 (2002). doi:10.1007/s005740200021

    Article  MathSciNet  MATH  Google Scholar 

  • Cappé, O., Moulines, E., Rydén, T.: Inference in Hidden Markov Models. Springer, New York (2009)

    MATH  Google Scholar 

  • Cooke, S., Hinch, S., Wikelski, M., Andrews, R., Kuchel, T., Wolcott, L.J., Butler, P.: Biotelemetry: a mechanistic approach to ecology. Trends Ecol. Evolut. 19(6), 334–343 (2004). doi:10.1016/j.tree.2004.04.003

    Article  Google Scholar 

  • Cooke, S., Midwood, J., Thiem, J., Klimley, P., Lucas, M., Thorstad, E., Eiler, J., Holbrook, C., Ebner, B.: Tracking animals in freshwater with electronic tags: past, present and future. Anim. Biotelem. 1(1), 1–1 (2013). doi:10.1186/2050-3385-1-5

    Article  Google Scholar 

  • de Jager, M., Weissing, F.J., Herman, P.M., Nolet, B.A., van de Koppel, J.: Lévy walks evolve through interaction between movement and environmental complexity. Science 332(6037), 1551–1553 (2011)

    Article  Google Scholar 

  • DeRuiter, S., Langrock, R., Skirbutas, T., Goldbogen, J., Calambokidis, J., Friedlaender, A., Southall, B.: A multivariate mixed hmm for analyzing the effect of sonar exposure on the behavioural state-switching dynamics of blue whales. arXiv preprint, arXiv:1602.06570 (2016)

  • Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Stat. Comput. 10, 197–208 (2000)

    Article  Google Scholar 

  • Doucet, A., de Freitas, N., Gordon, N. (eds.): An introduction to sequential monte carlo methods. In: Sequential Monte Carlo Methods in Practice, pp. 582. Springer, New York (2001)

  • Dowd, M., Joy, R.: Estimating behavioural parameters in animal movement models using a state-augmented particle filter. Ecology 92, 568–575 (2011)

    Article  Google Scholar 

  • Dunn, J., Gipson, P.: Analysis of radio telemetry data in studies of home range. Biometrics 33(1), 85–101 (1977). doi:10.2307/2529305

    Article  MATH  Google Scholar 

  • Edwards, A.: Revisiting lévy flight search patterns of wandering albatrosses, bumblebees and deer. Nature 449, 1044–1048 (2007). doi:10.1038/nature06199

    Article  Google Scholar 

  • Edwards, A.: Overturning conclusions of lévy flight movement patterns by fishing boats and foraging animals. Ecology 92(6), 1247–1257 (2011). doi:10.1890/10-1182.1

    Article  Google Scholar 

  • Edwards, A.M., Freeman, M.P., Breed, G.A., Jonsen, I.D.: Incorrect likelihood methods were used to infer scaling laws of marine predator search behaviour. PLoS ONE 7(10), e45174–e45174 (2012)

    Article  Google Scholar 

  • Eveson, J.P., Basson, M., Hobday, A.J.: Using electronic tag data to improve mortality and movement estimates in a tag-based spatial fisheries assessment model. Can. J. Fish. Aquat. Sci. 69(5), 869–883 (2012)

    Article  Google Scholar 

  • Fearnhead, P.: Mcmc for state-space models. In: Brooks, S.P., Gelman, A., Jones, G.L., Meng, X. (eds.) Handbook of Markov Chain Monte Carlo, Handbook of Modern Statistical Methods, pp. 513–529. Chapman & Hall/CRC, Boca Raton (2011)

    Google Scholar 

  • Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., Sibert, J.: Ad model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27(2), 233–249 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  • Franke, A., Caelli, T., Hudson, R.: Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models. Ecol. Model. 173(2–3), 259–270 (2004). doi:10.1016/j.ecolmodel.2003.06.004

    Article  Google Scholar 

  • Guttorp, P.: Stochastic Modelling of Scientific Data. Chapman and Hall/CRC, Boca Raton (1995)

    Book  MATH  Google Scholar 

  • Harris, K.J., Blackwell, P.G.: Flexible continuous-time modelling for heterogeneous animal movement. Ecol. Model. 255, 29–37 (2013). doi:10.1016/j.ecolmodel.2013.01.020

    Article  Google Scholar 

  • Harvey, A.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge (1990)

    Book  MATH  Google Scholar 

  • Holzmann, H., Munk, A., Suster, M., Zucchini, W.: Hidden markov models for circular and linear-circular time series. Environ. Ecol. Stat. 13(3), 325–347 (2006). doi:10.1007/s10651-006-0015-7

    Article  MathSciNet  Google Scholar 

  • Horne, J., Garton, E., Krone, S., Lewis, J.: Analyzing animal movements using Brownian bridges. Ecology 88(9), 2354–2363 (2007). doi:10.1890/06-0957.1

    Article  Google Scholar 

  • Humphries, N.E., Queiroz, N., Dyer, J.R., Pade, N.G., Musyl, M.K., Schaefer, K.M., Fuller, D.W., Brunnschweiler, J.M., Doyle, T.K., Houghton, J.D., et al.: Environmental context explains lévy and brownian movement patterns of marine predators. Nature 465(7301), 1066–1069 (2010)

    Article  Google Scholar 

  • Jackson, C., Sharples, L.: Hidden markov models for the onset and progression of bronchiolitis obliterans syndrome in lung transplant recipients. Stat. Med. 21(1), 113–128 (2002). doi:10.1002/sim.886

    Article  Google Scholar 

  • Jennrich, R., Turner, F.: Measurement of non-circular home range. J. Theor. Biol. 22(2), 227–237 (1969). doi:10.1016/0022-5193(69)90002-2

    Article  Google Scholar 

  • Johnson, D., London, J., Lea, M., Durban, J.: Continuous-time correlated random walk model for animal telemetry data. Ecology 89(5), 1208–1215 (2008). doi:10.1890/07-1032.1

    Article  Google Scholar 

  • Jonsen, I., Flemming, J., Myers, R.: Robust state-space modeling of animal movement data. Ecology 86(11), 2874–2880 (2005). doi:10.1890/04-1852

    Article  Google Scholar 

  • Jonsen, I., Myers, R., James, M.: Robust hierarchical state-space models reveal diel variation in travel rates of migrating leatherback turtles. J. Anim. Ecol. 75(5), 1046–1057 (2006). doi:10.1111/j.1365-2656.2006.01129.x

    Article  Google Scholar 

  • Jonsen, I., Basson, M., Bestley, S., Bravington, M., Patterson, T., Pedersen, M., Thomson, R., Thygesen, U., Wotherspoon, S.: State-space models for bio-loggers: a methodological road map. Deep Sea Res. II Top. Stud. Oceanogr. 88–89, 34–46 (2013). doi:10.1016/j.dsr2.2012.07.008

    Article  Google Scholar 

  • Kalman, R.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960). doi:10.1115/1.3662552

    Article  Google Scholar 

  • Kantas, N., Doucet, A., Singh, S., Maciejowski, J., Chopin, N.: On particle methods for parameter estimation in state-space models. Stat. Sci. 30, 328–351 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  • Kuhn, C., Johnson, D., Ream, R., Gelatt, T.: Advances in the tracking of marine species: using GPS locations to evaluate satellite track data and a continuous-time movement model. Mar. Ecol. Prog. Ser. 393, 97–109 (2009). doi:10.3354/meps08229

    Article  Google Scholar 

  • Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., Morales, J.: Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology 93(11), 2336–2342 (2012). doi:10.1890/11-2241.1

    Article  Google Scholar 

  • Langrock, R., Hopcraft, J., Blackwell, P., Goodall, V., King, R., Niu, M., Patterson, T., Pedersen, M., Skarin, A., Schick, R.: Modelling group dynamic animal movement. Methods Ecol. Evolut. 5(2), 190–199 (2014). doi:10.1111/2041-210X.12155

    Article  Google Scholar 

  • Laplanche, C., Marques, T.A., Thomas, L.: Tracking marine mammals in 3D using electronic tag data. Methods Ecol. Evolut. 6(9), 987–996 (2015)

    Article  Google Scholar 

  • Leos-Barajas, V., Photopoulou, T., Langrock, R., Patterson, T.A., Watanabe, Y., Murgatroyd, M., Papastamatiou, Y.: Analysis of animal accelerometer data using hidden Markov models. Methods Ecol. Evolut. (2016). doi:10.1111/2041-210X.12657

    Google Scholar 

  • Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2004)

    Book  Google Scholar 

  • Lopez, R., Malarde, J.-P., Royer, F., Gaspar, P.: Improving argos doppler location using multiple-model kalman filtering. IEEE Trans. Geosci. Remote Sens. 52(8), 4744–4755 (2014)

    Article  Google Scholar 

  • Lunn, D., Thomas, A., Best, N., Spiegelhalter, D.: WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10(4), 325–337 (2000). doi:10.1023/A:1008929526011

    Article  Google Scholar 

  • MacDonald, I.: Numerical maximisation of likelihood: a neglected alternative to em? Int. Stat. Rev. 82(2), 296–308 (2014). doi:10.1111/insr.12041

    Article  MathSciNet  Google Scholar 

  • Madon, B., Hingrat, Y.: Deciphering behavioral changes in animal movement with a ‘multiple change point algorithm-classification tree’ framework. Front. Ecol. Evolut. 2, 30 (2014)

    Google Scholar 

  • Marsh, L., Jones, R.: The form and consequences of random walk movement models. J. Theor. Biol. 133(1), 113–131 (1988). doi:10.1016/S0022-5193(88)80028-6

    Article  Google Scholar 

  • Maunder, M.N., Schnute, J.T., Ianelli, J.: Computers in fisheries population dynamics. In: Megrey, B.A., Moksness, E. (eds.) Computers in Fisheries Research, 2nd edn. Springer, New York (2009)

    Google Scholar 

  • McClintock, B., King, R., Thomas, L., Matthiopoulos, J., McConnell, B., Morales, J.: A general discrete-time modeling framework for animal movement using multistate random walks. Ecol. Monogr. 82(3), 335–349 (2012). doi:10.1890/11-0326.1

    Article  Google Scholar 

  • McClintock, B., Johnson, D., Hooten, M., Ver Hoef, J., Morales, J.: When to be discrete: the importance of time formulation in understanding animal movement. Mov. Ecol. 2(1), 1–21 (2014). doi:10.1186/s40462-014-0021-6

    Article  Google Scholar 

  • McGowan, J., Beger, M., Lewison, R.L., Harcourt, R., Campbell, H., Priest, M., Dwyer, R.G., Lin, H.-Y., Lentini, P., Dudgeon, C., et al.: Integrating research using animal-borne telemetry with the needs of conservation management. J. Appl. Ecol. 54(2), 423–429 (2016)

    Article  Google Scholar 

  • McKellar, A., Langrock, R., Walters, J., Kesler, D.: Using mixed hidden Markov models to examine behavioral states in a cooperatively breeding bird. Behav. Ecol. 26(1), 148–157 (2015). doi:10.1093/beheco/aru171

    Article  Google Scholar 

  • Meinhold, R.J., Singpurwalla, N.D.: Robustification of kalman filter models. J. Am. Stat. Assoc. 84(406), 479–486 (1989)

    Article  MathSciNet  Google Scholar 

  • Michelot, T., Langrock, R., Patterson, T.A.: moveHMM: an R package for analysing animal movement data using hidden markov models. Methods Ecol. Evolut. 7, 1308–1315 (2016)

    Article  Google Scholar 

  • Morales, J., Haydon, D., Frair, J., Holsiner, K., Fryxell, J.: Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85(9), 2436–2445 (2004). doi:10.1890/03-0269

    Article  Google Scholar 

  • Musyl, M., Domeier, M., Nasby-Lucas, N., Brill, R., McNaughton, L., Swimmer, J., Lutcavage, M., Wilson, S., Galuardi, B., Liddle, J.: Performance of pop-up satellite archival tags. Mar. Ecol. Prog. Ser. 433, 1–28 (2011)

    Article  Google Scholar 

  • Nielsen, A., Sibert, J.R.: State-space model for light-based tracking of marine animals. Can. J. Fish. Aquat. Sci. 64(8), 1055–1068 (2007)

    Article  Google Scholar 

  • Nielsen, A., Bigelow, K.A., Musyl, M.K., Sibert, J.R.: Improving light-based geolocation by including sea surface temperature. Fish. Oceanogr. 15(4), 314–325 (2006)

    Article  Google Scholar 

  • Niu, M., Blackwell, P.G., Skarin, A.: Modeling interdependent animal movement in continuous time. Biometrics 72, 315–324 (2016). doi:10.1111/biom.12454

    Article  MathSciNet  MATH  Google Scholar 

  • Pagendam, D., Ross, J., Chan, F., Marinova, D., Anderssen, R.: Optimal GPS tracking for estimating species movements. In: International Congress on Modelling and Simulation (19th: 2011: Perth, Australia) (2011)

  • Parton, A., Blackwell, P., Skarin, A.: Bayesian inference for continuous time animal movement based on steps and turns. arXiv preprint, arXiv:1608.05583 (2016)

  • Patterson, T.A., Hartmann, K.: Designing satellite tagging studies: estimating and optimizing data recovery. Fish. Oceanogr. 20(6), 449–461 (2011)

    Article  Google Scholar 

  • Patterson, T., Thomas, L., Wilcox, C., Ovaskainen, O., Matthiopoulos, J.: State-space models of individual animal movement. Trends Ecol. Evolut. 23(2), 87–94 (2008). doi:10.1016/j.tree.2007.10.009

    Article  Google Scholar 

  • Patterson, T., Basson, M., Bravington, M., Gunn, J.: Classifying movement behaviour in relation to environmental conditions using hidden Markov models. J. Anim. Ecol. 78(6), 1113–1123 (2009). doi:10.1111/j.1365-2656.2009.01583.x

    Article  Google Scholar 

  • Patterson, T., McConnell, B., Fedak, M., Bravington, M., Hindell, M.: Using GPS data to evaluate the accuracy of state-space methods for correction of Argos satellite telemetry error. Ecology 91(1), 273–285 (2010). doi:10.1890/08-1480.1

    Article  Google Scholar 

  • Pedersen, M.W., Righton, D., Thygesen, U.H., Andersen, K.H., Madsen, H.: Geolocation of North Sea cod (Gadus morhua) using hidden markov models and behavioural switching. Can. J. Fish. Aquat. Sci. 65(11), 2367–2377 (2008)

    Article  Google Scholar 

  • Pedersen, M., Patterson, T., Thygesen, U., Madsen, H.: Estimating animal behaviour and residency from movement data. Oikos 120(9), 1281–1290 (2011). doi:10.1111/j.1600-0706.2011.19044.x

    Article  Google Scholar 

  • Pedersen, M.W., Berg, C.W., Thygesen, U.H., Nielsen, A., Madsen, H.: Estimation methods for nonlinear state-space models in ecology. Ecol. Model. 222(8), 1394–1400 (2011b)

    Article  Google Scholar 

  • Plummer, M.: JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (2003)

  • Preisler, H., Ager, A., Johnson, B., Kie, J.: Modeling animal movements using stochastic differential equations. Environmetrics 15(7), 643–657 (2004). doi:10.1002/env.636

    Article  Google Scholar 

  • Preisler, H., Ager, A., Wisdom, M.: Analyzing animal movement patterns using potential functions. Ecosphere 4(3), art32 (2013). doi:10.1890/ES12-00286.1

    Article  Google Scholar 

  • Pyke, G.: Understanding movements of organisms: it’s time to abandon the lévy foraging hypothesis. Methods Ecol. Evolut. 6(1), 1–16 (2015). doi:10.1111/2041-210X.12298

    Article  Google Scholar 

  • Russell, J.C., Hanks, E.M., Haran, M.: Dynamic models of animal movement with spatial point process interactions. J. Agric. Biol. Environ. Stat. 21(1), 22–40 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  • Rutz, C., Hays, G.: New frontiers in biologging science. Biol. Lett. 5(3), 289–292 (2009). doi:10.1098/rsbl.2009.0089

    Article  Google Scholar 

  • Scharf, H.R., Hooten, M.B., Fosdick, B.K., Johnson, D.S., London, J.M., Durban, J.W.: Dynamic social networks based on movement. arXiv preprint arXiv: 1512.07607 (2015)

  • Sibert, J.R., Musyl, M.K., Brill, R.W.: Horizontal movements of bigeye tuna (Thunnus obesus) near hawaii determined by Kalman filter analysis of archival tagging data. Fish. Oceanogr. 12(3), 141–151 (2003)

    Article  Google Scholar 

  • Sims, D.W., Southall, E.J., Humphries, N.E., Hays, G.C., Bradshaw, C.J., Pitchford, J.W., James, A., Ahmed, M.Z., Brierley, A.S., Hindell, M.A., et al.: Scaling laws of marine predator search behaviour. Nature 451(7182), 1098–1102 (2008)

    Article  Google Scholar 

  • Sippel, T., Eveson, J.P., Galuardi, B., Lam, C., Hoyle, S., Maunder, M., Kleiber, P., Carvalho, F., Tsontos, V., Teo, S.L., et al.: Using movement data from electronic tags in fisheries stock assessment: a review of models, technology and experimental design. Fish. Res. 163, 152–160 (2015)

    Article  Google Scholar 

  • Stepanov, S.S.: Stochastic World. Springer, New York (2013)

    Book  MATH  Google Scholar 

  • Sur, M., Skidmore, A.K., Exo, K.-M., Wang, T., Ens, B.J., Toxopeus, A.: Change detection in animal movement using discrete wavelet analysis. Ecol. Inform. 20, 47–57 (2014)

    Article  Google Scholar 

  • Thygesen, U.H., Pedersen, M.W., Madsen, H.: Geolocating fish using hidden markov models and data storage tags. In: Nielsen, J.L., Arrizabalaga, H., Fragoso, N., Hobday, A., Lutcavage, M., Sibert, J. (eds.) Tagging and Tracking of Marine Animals with Electronic Devices, pp. 277–293. Springer, Netherlands (2009)

    Chapter  Google Scholar 

  • Towner, A., Leos-Barajas, V., Langrock, R., Schick, R., Smale, M., Jewell, O., Kaschke, T., Papastamatiou, Y.: Sex-specific and individual preferences for hunting strategies in white sharks. Funct. Ecol. 30, 1397–1407 (2016)

    Article  Google Scholar 

  • Uhlenbeck, G., Ornstein, L.: On the theory of the Brownian motion. Phys. Rev. 36(5), 0823–0841 (1930). doi:10.1103/PhysRev.36.823

    Article  Google Scholar 

  • van de Kerk, M., Onorato, D., Criffield, M., Bolker, B., Augustine, B., McKinley, S., Oli, M.: Hidden semi-markov models reveal multiphasic movement of the endangered florida panther. J. Anim. Ecol. 84(2), 576–585 (2015). doi:10.1111/1365-2656.12290

    Article  Google Scholar 

  • Viswanathan, G., Buldyrev, S.V., Havlin, S., Da Luz, M., Raposo, E., Stanley, H.E.: Optimizing the success of random searches. Nature 401(6756), 911–914 (1999)

    Article  Google Scholar 

  • Wikle, C.K., Berliner, L.M.: A bayesian tutorial for data assimilation. Phys. D Nonlinear Phenom. 230(1), 1–16 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Wilmers, C., Nickel, B., Bryce, C., Smith, J., Wheat, R., Yovovich, V.: The golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. Ecology 96(7), 1741–1753 (2015). doi:10.1890/14-1401.1

    Article  Google Scholar 

  • Zhang, J., O’Reilly, K.M., Perry, G.L., Taylor, G.A., Dennis, T.E.: Extending the functionality of behavioural change-point analysis with k-means clustering: a case study with the little penguin (Eudyptula minor). PLoS ONE 10, e0122811 (2015)

    Article  Google Scholar 

  • Zucchini, W., MacDonald, I., Langrock, R.: Hidden Markov Models for Time Series: An Introduction Using R, 2nd edn. Chapman and Hall/CRC, Boca Raton (2016)

    MATH  Google Scholar 

Download references

Acknowledgements

We thank Geoff Hosack and two anonymous referees for their useful feedback on this manuscript. We also gratefully acknowledge the editorial input of David Borchers which greatly improved and clarified the points made in this paper.

Author information

Authors and Affiliations

  1. CSIRO Oceans and Atmosphere, Hobart, Australia

    Toby A. Patterson

  2. School of Mathematics and Statistics, University of Sheffield, Sheffield, UK

    Alison Parton & Paul G. Blackwell

  3. Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany

    Roland Langrock

  4. School of Mathematics and Statistics, University of St Andrews, St Andrews, UK

    Len Thomas

  5. School of Mathematics, University of Edinburgh, Edinburgh, UK

    Ruth King

Authors
  1. Toby A. Patterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alison Parton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roland Langrock
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul G. Blackwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Len Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ruth King
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toby A. Patterson.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patterson, T.A., Parton, A., Langrock, R. et al. Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges. AStA Adv Stat Anal 101, 399–438 (2017). https://doi.org/10.1007/s10182-017-0302-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10182-017-0302-7

Keywords

Search

Navigation