Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement

Estimating the Dimension of a Model

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Strictly Proper Scoring Rules, Prediction, and Estimation

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EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS

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Hidden Markov Models for Time Series: An Introduction Using R

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Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States

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An Introduction to Model Selection

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

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Sensitivity and specificity of information criteria

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Uncovering ecological state dynamics with hidden Markov models

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Understanding the ontogeny of foraging behaviour: insights from combining marine predator bio-logging with satellite-derived oceanography in hidden Markov models

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Hidden Markov models: Pitfalls and opportunities in ecology

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Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models

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Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements

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State‐switching continuous‐time correlated random walks

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Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories

Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R. (2017) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2017.2723278 · Cited by 74

Bridging the gaps in animal movement: hidden behaviors and ecological relationships revealed by integrated data streams

McClintock, B. T., London, J. M., Cameron, M. F., & Boveng, P. L. (2017) · Ecosphere · doi:10.1002/ecs2.1751 · Cited by 68

Sex‐specific effects of wind on the flight decisions of a sexually dimorphic soaring bird

Clay, T. A., Joo, R., Weimerskirch, H., Phillips, R. A., den Ouden, O. F. C., Basille, M., Clusella‐Trullas, S., Assink, J. D., & Patrick, S. C. (2020) · Journal of Animal Ecology · doi:10.1111/1365-2656.13267 · Cited by 73

Behavioral state resource selection in invasive wild pigs in the Southeastern United States

Clontz, L. M., Pepin, K. M., VerCauteren, K. C., & Beasley, J. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-86363-3 · Cited by 59

Energetics as common currency for integrating high resolution activity patterns into dynamic energy budget-individual based models

Chimienti, M., Desforges, J.-P. W., Beumer, L. T., Nabe‐Nielsen, J., van Beest, F. M., & Schmidt, N. M. (2020) · Ecological Modelling · doi:10.1016/j.ecolmodel.2020.109250 · Cited by 57

Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

Conners, M. G., Michelot, T., Heywood, E. I., Orben, R. A., Phillips, R. A., Vyssotski, A. L., Shaffer, S. A., & Thorne, L. H. (2021) · Movement Ecology · doi:10.1186/s40462-021-00243-z · Cited by 56

Flexible hidden Markov models for behaviour-dependent habitat selection

Klappstein, N. J., Thomas, L., & Michelot, T. (2023) · Movement Ecology · doi:10.1186/s40462-023-00392-3 · Cited by 53

Effects of a seismic survey on movement of free-ranging Atlantic cod

van der Knaap, I., Reubens, J., Thomas, L., Ainslie, M. A., Winter, H. V., Hubert, J., Martin, B. S., & Slabbekoorn, H. (2021) · Current Biology · doi:10.1016/j.cub.2021.01.050 · Cited by 53

Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models

Bacheler, N., Michelot, T., Cheshire, R. T., & Shertzer, K. (2019) · Fisheries Research · doi:10.1016/j.fishres.2019.02.014 · Cited by 38

Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator

Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P. M., Páez‐Rosas, D., & Krüger, O. (2021) · Oecologia · doi:10.1007/s00442-021-04850-w · Cited by 46

Worth the effort? A practical examination of random effects in hidden Markov models for animal telemetry data

McClintock, B. T. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13619 · Cited by 45

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Charfi, S., Ansari, M. E., & Balasingham, I. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6232 · Cited by 42

An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore

Beumer, L. T., Pohle, J., Schmidt, N. M., Chimienti, M., Desforges, J.-P. W., Hansen, L. H., Langrock, R., Pedersen, S. H., Stelvig, M., & van Beest, F. M. (2020) · Movement Ecology · doi:10.1186/s40462-020-00213-x · Cited by 40

Commentary on latent class, latent profile, and latent transition analysis for characterizing individual differences in learning

Bray, B. C., & Dziak, J. J. (2018) · Learning and Individual Differences · doi:10.1016/j.lindif.2018.06.001 · Cited by 37

Seabirds as environmental indicators: foraging behaviour and ecophysiology of common diving petrels (Pelecanoides urinatrix) reflect local-scale differences in prey availability

Dunphy, B. J., Vickers, S., Zhang, J., Sagar, R. L., Landers, T. J., Bury, S. J., Hickey, A. J., & Rayner, M. J. (2020) · Marine Biology · doi:10.1007/s00227-020-3672-4 · Cited by 36

Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

Maruotti, A., Punzo, A., & Bagnato, L. (2018) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nby019 · Cited by 35

Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies

Maruotti, A., & Punzo, A. (2021) · International Statistical Review · doi:10.1111/insr.12436 · Cited by 35

Understanding decision making in a food-caching predator using hidden Markov models

Farhadinia, M. S., Michelot, T., Johnson, P. J., Hunter, L., & Macdonald, D. W. (2020) · Movement Ecology · doi:10.1186/s40462-020-0195-z · Cited by 34

Quantifying behavior and life‐history events of an Arctic ungulate from year‐long continuous accelerometer data

Chimienti, M., van Beest, F. M., Beumer, L. T., Desforges, J.-P. W., Hansen, L. H., Stelvig, M., & Schmidt, N. M. (2021) · Ecosphere · doi:10.1002/ecs2.3565 · Cited by 34

A copula-based multivariate hidden Markov model for modelling momentum in football

Ötting, M., Langrock, R., & Maruotti, A. (2021) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-021-00395-8 · Cited by 33

A hierarchical path-segmentation movement ecology framework

Getz, W. M. (2022) · Ecological Processes · doi:10.1186/s13717-022-00399-5 · Cited by 31

“Too Big To Ignore”: A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries

Cardiec, F., Bertrand, S., Witt, M. J., Metcalfe, K., Godley, B. J., McClellan, C., Vilela, R., Parnell, R. J., & Loc’h, F. L. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234091 · Cited by 31

Sex and Size Influence the Spatiotemporal Distribution of White Sharks, With Implications for Interactions With Fisheries and Spatial Management in the Southwest Indian Ocean

Kock, A. A., Lombard, A. T., Daly, R., Goodall, V., Meÿer, M. A., Johnson, R. L., Fischer, C., Koen, P., Irion, D. T., Gennari, E., et al. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.811985 · Cited by 31

GPS telemetry for small seabirds: using hidden Markov models to infer foraging behaviour of Common Diving Petrels (Pelecanoides urinatrix urinatrix)

Zhang, J., Rayner, M. J., Vickers, S., Landers, T. J., Sagar, R. L., Stewart, J., & Dunphy, B. J. (2019) · Emu - Austral Ornithology · doi:10.1080/01584197.2018.1558997 · Cited by 37

The hidden brain-state dynamics of tACS aftereffects

Kasten, F. H., & Herrmann, C. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119713 · Cited by 31

Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Bagi, B., Brecht, M., & Sanguinetti-Scheck, J. I. (2022) · Current Biology · doi:10.1016/j.cub.2022.04.068 · Cited by 27

Spline‐based nonparametric inference in general state‐switching models

Langrock, R., Adam, T., Barajas, V. L., Mews, S., Miller, D. L., & Papastamatiou, Y. P. (2018) · Statistica Neerlandica · doi:10.1111/stan.12133 · Cited by 27

Incorporating movement patterns to discern habitat selection: black bears as a case study

Karelus, D. L., McCown, J. W., Scheick, B. K., van de Kerk, M., Bolker, B. M., & Oli, M. K. (2019) · Wildlife Research · doi:10.1071/wr17151 · Cited by 27

Caution is warranted when using animal space-use and movement to infer behavioral states

Buderman, F. E., Gingery, T. M., Diefenbach, D. R., Gigliotti, L. C., Begley-Miller, D., McDill, M. E., Wallingford, B. D., Rosenberry, C. S., & Drohan, P. J. (2021) · Movement Ecology · doi:10.1186/s40462-021-00264-8 · Cited by 27

Activity modulation and selection for forests help giant anteaters to cope with temperature changes

Giroux, A., Ortega, Z., Attias, N., Desbiez, A. L. J., do Valle, D. R., Börger, L., & Oliveira‐Santos, L. G. R. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.04.008 · Cited by 27

Understanding narwhal diving behaviour using Hidden Markov Models with dependent state distributions and long range dependence

Ngô, M. C., Heide‐Jørgensen, M. P., & Ditlevsen, S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006425 · Cited by 26

Identifying latent behavioural states in animal movement with M4, a nonparametric Bayesian method

Cullen, J. A., Poli, C. L., Fletcher, R. J., & do Valle, D. R. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13745 · Cited by 26

Dynamic-Parameter Movement Models Reveal Drivers of Migratory Pace in a Soaring Bird

Eisaguirre, J. M., Auger‐Méthé, M., Barger, C. P., Lewis, S. B., Booms, T. L., & Breed, G. A. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2019.00317 · Cited by 26

Effects of 2D Seismic on Snow Crab Movement Behavior

Côté, D., Morris, C. J., Regular, P. M., & Piersiak, M. G. (2020) · Fisheries Research · doi:10.1016/j.fishres.2020.105661 · Cited by 25

Searching on the edge: dynamic oceanographic features increase foraging opportunities in a small pelagic seabird

Pascalis, F. D., Pála, D., Pisu, D., Morinay, J., Benvenuti, A., Spano, C., Ruiu, A., Serra, L., Rubolini, D., & Cecere, J. G. (2021) · Marine Ecology Progress Series · doi:10.3354/meps13726 · Cited by 30

Evaluating the constraints governing activity patterns of a coastal marine top predator

Byrnes, E. E., Daly, R., Barajas, V. L., Langrock, R., & Gleiss, A. C. (2021) · Marine Biology · doi:10.1007/s00227-020-03803-w · Cited by 25

Modelling state‐transition dynamics in resting‐state brain signals by the hidden Markov and Gaussian mixture models

Ezaki, T., Himeno, Y., Watanabe, T., & Masuda, N. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15386 · Cited by 23

A Joint Modeling Approach for Childhood Meat, Fish and Egg Consumption and the Risk of Advanced Islet Autoimmunity

Syrjälä, E., Nevalainen, J., Peltonen, J., Takkinen, H., Hakola, L., Erlund, I., Veijola, R., Ilonen, J., Toppari, J., Knip, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44196-1 · Cited by 22

Hidden semi-Markov-switching quantile regression for time series

Maruotti, A., Petrella, L., & Sposito, L. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107208 · Cited by 21

Automatic selection of the number of clusters using Bayesian clustering and sparsity‐inducing priors

do Valle, D. R., Jameel, Y., Betancourt, B., Azeria, E. T., Attias, N., & Cullen, J. A. (2021) · Ecological Applications · doi:10.1002/eap.2524 · Cited by 21

Quantile hidden semi-Markov models for multivariate time series

Merlo, L., Maruotti, A., Petrella, L., & Punzo, A. (2022) · Statistics and Computing · doi:10.1007/s11222-022-10130-1 · Cited by 21

Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata)

Hewitt, D. E., Johnson, D. D., Suthers, I. M., & Taylor, M. D. (2023) · Movement Ecology · doi:10.1186/s40462-023-00384-3 · Cited by 21

Effects of seismic airgun playbacks on swimming patterns and behavioural states of Atlantic cod in a net pen

Hubert, J., Campbell, J., & Slabbekoorn, H. (2020) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2020.111680 · Cited by 20

Integrated Population Models: Achieving Their Potential

Frost, F., McCrea, R. S., King, R., Giménez, O., & Zipkin, E. F. (2022) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-022-00302-7 · Cited by 24

Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning–Based Ecological Momentary Assessment Study

Ryu, J., Sükei, E., Norbury, A., Liu, S. H., Campaña-Montes, J. J., Baca‐García, E., Artés-Rodríguez, A., & Pérez-Rodríguez, M. M. (2021) · JMIR Mental Health · doi:10.2196/30833 · Cited by 20

Football tracking data: a copula-based hidden Markov model for classification of tactics in football

Ötting, M., & Karlis, D. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04660-0 · Cited by 21

Drivers of polar bear behavior and the possible effects of prey availability on foraging strategy

Togunov, R. R., Derocher, A. E., Lunn, N. J., & Auger‐Méthé, M. (2022) · Movement Ecology · doi:10.1186/s40462-022-00351-4 · Cited by 21

Beetles on the move: Not‐just‐a‐technical review of beetles' radio‐tracking

Růžičková, J., & Elek, Z. (2022) · Entomologia Experimentalis et Applicata · doi:10.1111/eea.13260 · Cited by 19

How to account for behavioral states in step-selection analysis: a model comparison

Pohle, J., Signer, J., Eccard, J. A., Dammhahn, M., & Schlägel, U. E. (2024) · PeerJ · doi:10.7717/peerj.16509 · Cited by 19

A Time-Series Model for Underdispersed or Overdispersed Counts

MacDonald, I. L., & Bhamani, F. (2018) · The American Statistician · doi:10.1080/00031305.2018.1505656 · Cited by 18

Modelling multi‐scale, state‐switching functional data with hidden Markov models

Sidrow, E., Heckman, N., Fortune, S. M. E., Trites, A. W., Murphy, I., & Auger‐Méthé, M. (2021) · Canadian Journal of Statistics · doi:10.1002/cjs.11673 · Cited by 17

Validating Corruption Risk Measures: A Key Step to Monitoring SDG Progress

Gnaldi, M., & Sarto, S. D. (2023) · Social Indicators Research · doi:10.1007/s11205-023-03238-y · Cited by 17

Towards scalable deployment of Hidden Markov models in occupancy estimation: A novel methodology applied to the study case of occupancy detection

Ali, S., & Bouguila, N. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111594 · Cited by 17

Occupancy states forecasting with a hidden Markov model for incomplete data, exploiting daily periodicity

Kabbaj, O. A., Péan, L.-M., Masson, J.-B., Marhic, B., & Delahoche, L. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112985 · Cited by 20

The Conditionally Autoregressive Hidden Markov Model (CarHMM): Inferring Behavioural States from Animal Tracking Data Exhibiting Conditional Autocorrelation

Lawler, E., Whoriskey, K., Aeberhard, W. H., Field, C., & Flemming, J. M. (2019) · Journal of Agricultural Biological and Environmental Statistics · doi:10.1007/s13253-019-00366-2 · Cited by 16

Movement patterns and activity levels are shaped by the neonatal environment in Antarctic fur seal pups

Nagel, R., Mews, S., Adam, T., Stainfield, C., Fox‐Clarke, C., Toscani, C., Langrock, R., Forcada, J., & Hoffman, J. I. (2021) · Scientific Reports · doi:10.1038/s41598-021-93253-1 · Cited by 16

Behavioural responses of fin whales to military mid-frequency active sonar

Southall, B. L., Allen, A. N., Calambokidis, J., Casey, C. B., DeRuiter, S. L., Fregosi, S., Friedlaender, A. S., Goldbogen, J. A., Harris, C. M., Hazen, E. L., et al. (2023) · Royal Society Open Science · doi:10.1098/rsos.231775 · Cited by 16

A guide to state–space modeling of ecological time series

Auger‐Méthé, M., Newman, K. B., Cole, D. J., Empacher, F., Gryba, R., King, A. A., Barajas, V. L., Flemming, J. M., Nielsen, A., Petris, G., et al. (2021) · Ecological Monographs · doi:10.1002/ecm.1470 · Cited by 335

Unsealing behaviour: Variation in harbour seal (Phoca vitulina) responses to anthropogenic sound in relation to individual health

Maurer, N., Boyi, J. O., Schick, L. A., Nachtsheim, D. A., Schaffeld, T., Groß, S., Teilmann, J., Schnitzler, J., & Siebert, U. (2025) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2025.117777 · Cited by 15

A framework for integrating inferred movement behavior into disease risk models

Dougherty, E. R., Seidel, D. P., Blackburn, J. K., Turner, W. C., & Getz, W. M. (2022) · Movement Ecology · doi:10.1186/s40462-022-00331-8 · Cited by 15

The Effect of Periodic Email Prompts on Participant Engagement With a Behavior Change mHealth App: Longitudinal Study

Agachi, E., Bijmolt, T. H. A., van Ittersum, K., & Mierau, J. O. (2023) · JMIR mhealth and uhealth · doi:10.2196/43033 · Cited by 15

Penalized estimation of flexible hidden Markov models for time series of counts

Adam, T., Langrock, R., & Weiß, C. H. (2019) · METRON · doi:10.1007/s40300-019-00153-6 · Cited by 15

Packet Loss Characterization Using Cross Layer Information and HMM for Wi-Fi Networks

da Silva, C. A. G., & Pedroso, C. M. (2022) · Sensors · doi:10.3390/s22228592 · Cited by 14

Adjustment of foraging trips and flight behaviour to own and partner mass and wind conditions by a far-ranging seabird

Clay, T. A., Hodum, P. J., Hagen, E. N., & Brooke, M. D. L. (2023) · Animal Behaviour · doi:10.1016/j.anbehav.2023.02.007 · Cited by 14

A hidden Markov model for continuous longitudinal data with missing responses and dropout

Pandolfi, S., Bartolucci, F., & Pennoni, F. (2023) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.202200016 · Cited by 14

Finding the number of latent states in hidden Markov models using information criteria

Buckby, J., Wang, T., Fletcher, D., Zhuang, J., Takeo, A., & Obara, K. (2023) · Environmental and Ecological Statistics · doi:10.1007/s10651-023-00584-5 · Cited by 13

Anomaly Detection in Smart Industrial Machinery Through Hidden Markov Models and Autoencoders

Sorostinean, R., Burghelea, Z., & Gellért, Á. (2024) · IEEE Access · doi:10.1109/access.2024.3400970 · Cited by 14

Bayesian non-parametric detection heterogeneity in ecological models

Turek, D., Wehrhahn, C., & Giménez, O. (2021) · Environmental and Ecological Statistics · doi:10.1007/s10651-021-00489-1 · Cited by 13

Emergence of the wrapped Cauchy distribution in mixed directional data

Bailey, J. D., & Codling, E. A. (2020) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-020-00380-7 · Cited by 13

Threat-Event Detection for Distributed Networks Based on Spatiotemporal Markov Random Field

Ma, H., Xie, Y., Tang, S., Hu, J., & Liu, X. (2020) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2020.3036664 · Cited by 12

Flexible Modelling of Diel and Other Periodic Variation in Hidden Markov Models

Feldmann, C. C., Mews, S., Coculla, A., Stanewsky, R., & Langrock, R. (2023) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-023-00342-7 · Cited by 12

A regularized hidden Markov model for analyzing the ‘hot shoe’ in football

Ötting, M., & Groll, A. (2021) · Statistical Modelling · doi:10.1177/1471082x211008014 · Cited by 12

A probabilistic framework for predicting disease dynamics: A case study of psychotic depression

Bueno, M. L. P., Hommersom, A., Lucas, P., & Janzing, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103232 · Cited by 12

Using accelerometry tags to quantify gray whale foraging behavior

Colson, K. M., Pirotta, E., New, L., Cade, D. E., Calambokidis, J., Bierlich, K. C., Bird, C. N., Ajó, A. F., Hildebrand, L., Trites, A. W., et al. (2024) · Marine Mammal Science · doi:10.1111/mms.13210 · Cited by 11

Fluctuations in Density-Dependent Selection Drive the Evolution of a Pace-of-Life Syndrome Within and Between Populations

Milles, A., Dammhahn, M., Jeltsch, F., Schlägel, U. E., & Grimm, V. (2021) · The American Naturalist · doi:10.1086/718473 · Cited by 11

Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging

Hu, M., McComb, C., & Goucher-Lambert, K. (2023) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060423000021 · Cited by 11

Movement and behavioral states of common carp (Cyprinus carpio) in response to a behavioral deterrent in a navigational lock

Raboin, M. J., Plumb, J. M., Sholtis, M. D., Smith, D. L., Jackson, P. R., Rivera, J. M., Suski, C. D., & Cupp, A. R. (2023) · Movement Ecology · doi:10.1186/s40462-023-00396-z · Cited by 11

A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models

Hambuckers, J., Kneib, T., Langrock, R., & Silbersdorff, A. (2018) · Quantitative Finance · doi:10.1080/14697688.2017.1417625 · Cited by 11

State‐space modeling reveals habitat perception of a small terrestrial mammal in a fragmented landscape

Gardiner, R. Z., Hamer, R. P., Barajas, V. L., Peñaherrera-Palma, C. R., Jones, M. E., & Johnson, C. N. (2019) · Ecology and Evolution · doi:10.1002/ece3.5519 · Cited by 11

Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico

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