ReviewEcology letters2020
Uncovering ecological state dynamics with hidden Markov models.
Review in Ecology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 57 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
57 citing papers in PubMed.
- Duration-aware unsupervised segmentation reveals collective movement modes in homing flights of pigeon flocks.Movement ecology · 2026Article
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Scale-Dependent Attraction of Invasive Raccoons to Bait Sites: Behavioural and Proximity Responses in a Post-Disaster Agricultural Landscape.Ecology and evolution · 2026Article
- The 'aukward' use of hidden Markov models in a pursuit-diving seabird: semi-supervised HMMs improve behavioural classification and energetic inference in auks.Movement ecology · 2026Article
- Assessment of goose-beaked whale responses to mid-frequency active sonar using a hierarchical hidden Markov model.Movement ecology · 2026Article
- Challenges and approaches in tracking and analyzing movement of ground-dwelling insects.Movement ecology · 2026Review
- Dynamic Functional Connectivity, Major Depression, and Suicidal Ideation in Children.Human brain mapping · 2026Article
- Inferring behavioural states from tracking data with hidden Markov models - a validation study using GPS video-camera collars.Movement ecology · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Ringed Seal (Ecology and evolution · 2026Article
- The Range-Resident Logistic Model: A New Framework to Formalise the Population-Dynamics Consequences of Range Residency.Ecology letters · 2025Article
- Effects of urbanisation on the movements of an arboreal specialist using hidden Markov models.Movement ecology · 2025Article
- Rest to roam: behavioural adaptations of tigers in anthropogenically altered landscapes.Movement ecology · 2025Article
- Biomimetic robots reveal flexible adjustment of sexual signalling in a wild invertebrate.Proceedings. Biological sciences · 2025Article
- Unlocking sea turtle diving behaviour from low-temporal resolution time-depth recorders.Scientific reports · 2025Article
- Energy landscapes direct the movement preferences of elephants.The Journal of animal ecology · 2025Article
- An introduction to statistical models used to characterize species-habitat associations with animal movement data.Movement ecology · 2025Review
- Seasonal movement behavior of domestic goats in response to environmental variability and time of day using Hidden Markov Models.Movement ecology · 2025Article
- Leveraging Massive Opportunistically Collected Datasets to Study Species Communities in Space and Time.Ecology letters · 2025Article
- Putting the health in hidden Markov models: incorporating allostatic load indices into movement ecology analyses.Conservation physiology · 2025Article
Corrections and comments
- Erratum issuedCorrigendum.2021
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.