Hidden Markov models: Pitfalls and opportunities in ecology
Abstract Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.13801 |
| _version_ | 1797766975739396096 |
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| author | Richard Glennie Timo Adam Vianey Leos‐Barajas Théo Michelot Theoni Photopoulou Brett T. McClintock |
| author_facet | Richard Glennie Timo Adam Vianey Leos‐Barajas Théo Michelot Theoni Photopoulou Brett T. McClintock |
| author_sort | Richard Glennie |
| collection | DOAJ |
| description | Abstract Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls. In this paper, we review five lesser known pitfalls one can encounter when using HMMs or their extensions to solve ecological problems: (a) violation of the snapshot property in continuous‐time HMMs; (b) biased inference from hierarchical HMMs when applied to temporally misaligned processes; (c) sensitive inference from using random effects to partially pool across heterogeneous individuals; (d) computational burden when using HMMs to approximate models with continuous state spaces; and (e) difficulty linking the hidden process to space or environment. This review is for ecologists and ecological statisticians familiar with HMMs, but who may be less aware of the problems that arise in more specialised applications. We demonstrate how each pitfall arises, by simulation or example, and discuss why this pitfall is important to consider. Along with identifying the problems, we highlight potential research opportunities and offer ideas that may help alleviate these pitfalls. Each of the methods we review are solutions to current ecological research problems. We intend for this paper to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but we also hope that by highlighting future research opportunities, we can inspire ecological statisticians to weaken these pitfalls and provide improved methods. |
| first_indexed | 2024-03-12T20:33:03Z |
| format | Article |
| id | doaj.art-35669c23c0804c37be2f818f89b49d32 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-12T20:33:03Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-35669c23c0804c37be2f818f89b49d322023-08-01T18:55:41ZengWileyMethods in Ecology and Evolution2041-210X2023-01-01141435610.1111/2041-210X.13801Hidden Markov models: Pitfalls and opportunities in ecologyRichard Glennie0Timo Adam1Vianey Leos‐Barajas2Théo Michelot3Theoni Photopoulou4Brett T. McClintock5Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UKCentre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UKDepartment of Statistical Sciences University of Toronto Toronto ON CanadaCentre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UKCentre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews UKMarine Mammal Laboratory NOAA‐NMFS Alaska Fisheries Science Center Seattle WA USAAbstract Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data where noisy, multivariate measurements are made of a hidden, ecological process, and where this hidden process is represented by a sequence of discrete states. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls. In this paper, we review five lesser known pitfalls one can encounter when using HMMs or their extensions to solve ecological problems: (a) violation of the snapshot property in continuous‐time HMMs; (b) biased inference from hierarchical HMMs when applied to temporally misaligned processes; (c) sensitive inference from using random effects to partially pool across heterogeneous individuals; (d) computational burden when using HMMs to approximate models with continuous state spaces; and (e) difficulty linking the hidden process to space or environment. This review is for ecologists and ecological statisticians familiar with HMMs, but who may be less aware of the problems that arise in more specialised applications. We demonstrate how each pitfall arises, by simulation or example, and discuss why this pitfall is important to consider. Along with identifying the problems, we highlight potential research opportunities and offer ideas that may help alleviate these pitfalls. Each of the methods we review are solutions to current ecological research problems. We intend for this paper to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but we also hope that by highlighting future research opportunities, we can inspire ecological statisticians to weaken these pitfalls and provide improved methods.https://doi.org/10.1111/2041-210X.13801animal movementcontinuous timehidden Markov modelhierarchical modelpopulation ecologyrandom effects |
| spellingShingle | Richard Glennie Timo Adam Vianey Leos‐Barajas Théo Michelot Theoni Photopoulou Brett T. McClintock Hidden Markov models: Pitfalls and opportunities in ecology Methods in Ecology and Evolution animal movement continuous time hidden Markov model hierarchical model population ecology random effects |
| title | Hidden Markov models: Pitfalls and opportunities in ecology |
| title_full | Hidden Markov models: Pitfalls and opportunities in ecology |
| title_fullStr | Hidden Markov models: Pitfalls and opportunities in ecology |
| title_full_unstemmed | Hidden Markov models: Pitfalls and opportunities in ecology |
| title_short | Hidden Markov models: Pitfalls and opportunities in ecology |
| title_sort | hidden markov models pitfalls and opportunities in ecology |
| topic | animal movement continuous time hidden Markov model hierarchical model population ecology random effects |
| url | https://doi.org/10.1111/2041-210X.13801 |
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