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

2018 ◽  
Vol 15 (143) ◽  
pp. 20180084 ◽  
Author(s):  
W. James Grecian ◽  
Jude V. Lane ◽  
Théo Michelot ◽  
Helen M. Wade ◽  
Keith C. Hamer
Keyword(s):  
Foraging Behaviour ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Extended Period ◽  
Foraging Strategies ◽  
Habitat Features ◽  
Marine Predator ◽  
Age Related ◽  
Frontal Activity

The development of foraging strategies that enable juveniles to efficiently identify and exploit predictable habitat features is critical for survival and long-term fitness. In the marine environment, meso- and sub-mesoscale features such as oceanographic fronts offer a visible cue to enhanced foraging conditions, but how individuals learn to identify these features is a mystery. In this study, we investigate age-related differences in the fine-scale foraging behaviour of adult (aged ≥ 5 years) and immature (aged 2–4 years) northern gannets Morus bassanus . Using high-resolution GPS-loggers, we reveal that adults have a much narrower foraging distribution than immature birds and much higher individual foraging site fidelity. By conditioning the transition probabilities of a hidden Markov model on satellite-derived measures of frontal activity, we then demonstrate that adults show a stronger response to frontal activity than immature birds, and are more likely to commence foraging behaviour as frontal intensity increases. Together, these results indicate that adult gannets are more proficient foragers than immatures, supporting the hypothesis that foraging specializations are learned during individual exploratory behaviour in early life. Such memory-based individual foraging strategies may also explain the extended period of immaturity observed in gannets and many other long-lived species.

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

2019 ◽  
Vol 119 (2) ◽  
pp. 126-137 ◽  
Author(s):  
Jingjing Zhang ◽  
Matt Rayner ◽  
Shae Vickers ◽  
Todd Landers ◽  
Rachael Sagar ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Foraging Behaviour ◽  
Markov Models ◽  
Hidden Markov ◽  
Gps Telemetry

Extending hidden Markov models to allow conditioning on previous observations

2018 ◽  
Vol 16 (05) ◽  
pp. 1850019 ◽  
Author(s):  
Ioannis A. Tamposis ◽  
Margarita C. Theodoropoulou ◽  
Konstantinos D. Tsirigos ◽  
Pantelis G. Bagos
Keyword(s):  
Hidden Markov Models ◽  
Probabilistic Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Simple Extension ◽  
Computational Molecular Biology ◽  
Current State ◽  
Observation Sequence ◽  
Hidden States

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%–8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

scholarly journals On Determining the Order of Markov Dependence of an Observed Process Governed by a Hidden Markov Model

2002 ◽  
Vol 10 (3) ◽  
pp. 241-251 ◽  
Author(s):  
R.J. Boys ◽  
D.A. Henderson
Keyword(s):  
Markov Chain ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Real Data ◽  
Information Criteria ◽  
Monte Carlo Algorithm ◽  
Model Parameters ◽  
Markov Dependence ◽  
Finite State

This paper describes a Bayesian approach to determining the order of a finite state Markov chain whose transition probabilities are themselves governed by a homogeneous finite state Markov chain. It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. The method we describe uses a Markov chain Monte Carlo algorithm to obtain samples from the (posterior) distribution for both the order of Markov dependence in the observed sequence and the other governing model parameters. These samples allow coherent inferences to be made straightforwardly in contrast to those which use information criteria. The methods are illustrated by their application to both simulated and real data sets.

scholarly journals A non-parametric hidden Markov model for climate state identification

2003 ◽  
Vol 7 (5) ◽  
pp. 652-667 ◽  
Author(s):  
M. F. Lambert ◽  
J. P. Whiting ◽  
A. V. Metcalfe
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Probability Distributions ◽  
Parametric Model ◽  
The State ◽  
Conditional Distributions ◽  
Wet And Dry Cycles ◽  
Non Parametric

Abstract. Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions

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

2020 ◽  
Author(s):  
Brett T. McClintock
Keyword(s):  
Hidden Markov Models ◽  
Random Effects ◽  
State Transition ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Random Effect ◽  
Costs And Benefits ◽  
State Process ◽  
Random Effect Models

AbstractHidden Markov models (HMMs) that include individual-level random effects have recently been promoted for inferring animal movement behaviour from biotelemetry data. These “mixed HMMs” come at significant cost in terms of implementation and computation, and discrete random effects have been advocated as a practical alternative to more computationally-intensive continuous random effects. However, the performance of mixed HMMs has not yet been sufficiently explored to justify their widespread adoption, and there is currently little guidance for practitioners weighing the costs and benefits of mixed HMMs for a particular research objective.I performed an extensive simulation study comparing the performance of a suite of fixed and random effect models for individual heterogeneity in the hidden state process of a 2-state HMM. I focused on sampling scenarios more typical of telemetry studies, which often consist of relatively long time series (30 – 250 observations per animal) for relatively few individuals (5 – 100 animals).I generally found mixed HMMs did not improve state assignment relative to standard HMMs. Reliable estimation of random effects required larger sample sizes than are often feasible in telemetry studies. Continuous random effect models performed reasonably well with data generated under discrete random effects, but not vice versa. Random effects accounting for unexplained individual variation can improve estimation of state transition probabilities and measurable covariate effects, but discrete random effects can be a relatively poor (and potentially misleading) approximation for continuous variation.When weighing the costs and benefits of mixed HMMs, three important considerations are study objectives, sample size, and model complexity. HMM applications often focus on state assignment with little emphasis on heterogeneity in state transition probabilities, in which case random effects in the hidden state process simply may not be worth the additional effort. However, if explaining variation in state transition probabilities is a primary objective and sufficient explanatory covariates are not available, then random effects are worth pursuing as a more parsimonious alternative to individual fixed effects.To help put my findings in context and illustrate some potential challenges that practitioners may encounter when applying mixed HMMs, I revisit a previous analysis of long-finned pilot whale biotelemetry data.

scholarly journals On modifications to the Poisson-triggered hidden Markov paradigm through partitioned empirical recurrence rates ratios and its applications to natural hazards monitoring

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Moinak Bhaduri
Keyword(s):  
Natural Hazards ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Conditional Distributions ◽  
Recurrence Rates ◽  
State Dependent ◽  
Risk Period

Abstract Hidden Markov models (HMMs), especially those with a Poisson density governing the latent state-dependent emission probabilities, have enjoyed substantial and undeniable success in modeling natural hazards. Classifications among these hazards, induced through quantifiable properties such as varying intensities or geographic proximities, often exist, enabling the creation of an empirical recurrence rates ratio (ERRR), a smoothing statistic that is gradually gaining currency in modeling literature due to its demonstrated ability in unearthing interactions. Embracing these tools, this study puts forth a refreshing monitoring alternative where the unobserved state transition probability matrix in the likelihood of the Poisson based HMM is replaced by the observed transition probabilities of a discretized ERRR. Analyzing examples from Hawaiian volcanic and West Atlantic hurricane interactions, this work illustrates how the discretized ERRR may be interpreted as an observed version of the unobserved hidden Markov chain that generates one of the two interacting processes. Surveying different facets of traditional inference such as global state decoding, hidden state predictions, one-out conditional distributions, and implementing related computational algorithms, we find that the latest proposal estimates the chances of observing a high-risk period, one threatening several hazards, more accurately than its established counterpart. Strongly intuitive and devoid of forbidding technicalities, the new prescription launches a vision of surer forecasts and stands versatile enough to be applicable to other types of hazard monitoring (such as landslides, earthquakes, floods), especially those with meager occurrence probabilities.

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

Oecologia ◽  
2021 ◽  
Vol 195 (2) ◽  
pp. 313-325
Author(s):  
Jonas F. L. Schwarz ◽  
Sina Mews ◽  
Eugene J. DeRango ◽  
Roland Langrock ◽  
Paolo Piedrahita ◽  
...  
Keyword(s):  
High Resolution ◽  
Individual Variation ◽  
Markov Models ◽  
Temporal Distribution ◽  
Intraspecific Variability ◽  
Hierarchical Cluster ◽  
Foraging Strategies ◽  
Strong Impact ◽  
Sea Lions ◽  
Marine Predator

AbstractForaging strategies are of great ecological interest, as they have a strong impact on the fitness of an individual and can affect its ability to cope with a changing environment. Recent studies on foraging strategies show a higher complexity than previously thought due to intraspecific variability. To reliably identify foraging strategies and describe the different foraging niches they allow individual animals to realize, high-resolution multivariate approaches which consider individual variation are required. Here we dive into the foraging strategies of Galápagos sea lions (Zalophus wollebaeki), a tropical predator confronted with substantial annual variation in sea surface temperature. This affects prey abundance, and El Niño events, expected to become more frequent and severe with climate change, are known to have dramatic effects on sea lions. This study used high-resolution measures of depth, GPS position and acceleration collected from 39 lactating sea lion females to analyze their foraging strategies at an unprecedented level of detail using a novel combination of automated broken stick algorithm, hierarchical cluster analysis and individually fitted multivariate hidden Markov models. We found three distinct foraging strategies (pelagic, benthic, and night divers), which differed in their horizontal, vertical and temporal distribution, most likely corresponding to different prey species, and allowed us to formulate hypotheses with regard to adaptive values under different environmental scenarios. We demonstrate the advantages of our multivariate approach and inclusion of individual variation to reliably gain a deeper understanding of the adaptive value and ecological relevance of foraging strategies of marine predators in dynamic environments.

scholarly journals Residence Time Analysis of RNA Polymerase Transcription Dynamics: A Bayesian Sticky HMM Approach

2020 ◽  
Author(s):  
Zeliha Kilic ◽  
Ioannis Sgouralis ◽  
Steve Pressé
Keyword(s):  
Rna Polymerase ◽  
Hidden Markov Models ◽  
Residence Time ◽  
Optical Trapping ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Transcription Elongation ◽  
Residence Times ◽  
Average Residence Time

ABSTRACTThe time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed “residence time”, reports on the initiation, elongation and termination stages of transcription. At the single molecule level, this information can be obtained from dual ultra-stable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAP’s transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov models that treats all residence times, for an E. Coli RNAP, on an equal footing without a priori discriminating between long and short residence times. In addition, our method has two additional advantages, we provide: full distributions around key point statistics; and directly treat the sequence-dependence of RNAP’s elongation rate.By applying our approach to experimental data, we find: no emergent separation between long and short residence times warranted by the data; force dependent average residence time transcription elongation dynamics; limited effects of GreB on average backtracking durations and counts; and a slight drop in the average residence time as a function of applied force in RNaseA’s presence.STATEMENT OF SIGNIFICANCEMuch of what we know about RNA Polymerase, and its associated transcription factors, relies on successfully discriminating between what are believed to be short and long residence times in the data. This is achieved by applying pause-detection algorithms to trace analysis. Here we propose a new method relying on Bayesian sticky hidden Markov models to interpret time traces provided by dual optical trapping experiments associated with transcription elongation of RNAP. Our method does not discriminate between short and long residence times from the offset in the analysis. It allows for DNA site-dependent transition probabilities of RNAP to neighboring sites (thereby accounting for chemical variability in site to site transitions) and does not demand any time trace pre-processing (such as denoising).

Sign in / Sign up

Export Citation Format

Share Document