scholarly journals A Continuous-Time Semi-Markov Model for Animal Movement in a Dynamic Environment

2018 ◽  
Author(s):  
Devin S. Johnson ◽  
Noel A. Pelland ◽  
Jeremy T. Sterling
Keyword(s):  
Model Selection ◽  
Continuous Time ◽  
Animal Movement ◽  
Dynamic Environment ◽  
Likelihood Approximation ◽  
Fur Seal ◽  
Posterior Model Probability ◽  
Model Probability ◽  
Dynamic Habitat ◽  
Selection Methodology

AbstractWe consider an extension to discrete-space continuous-time models animal movement that have previously be presented in the literature. The extension from a continuous-time Markov formulation to a continuous-time semi-Markov formulation allows for the inclusion of temporally dynamic habitat conditions as well as temporally changing movement responses by animals to that environment. We show that with only a little additional consideration, the Poisson likelihood approximation for the Markov version can still be used within the multiple imputation framework commonly employed for analysis of telemetry data. In addition, we consider a Bayesian model selection methodology with the imputation framework. The model selection method uses a Laplace approximation to the posterior model probability to provide a computationally feasible approach. The full methodology is then used to analyze movements of 15 northern fur seal (Callorhinus ursinus) pups with respect to surface winds, geostrophic currents, and sea surface temperature. The highest posterior model probabilities belonged to those models containing only winds and current, SST did not seem to be a significant factor for modeling their movement.

scholarly journals A continuous-time semi-Markov model for animal movement in a dynamic environment

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Devin Johnson ◽  
Noel Pelland ◽  
Jeremy Sterling
Keyword(s):  
Markov Model ◽  
Continuous Time ◽  
Animal Movement ◽  
Dynamic Environment

Uncertainty of prior and posterior model probability: Implications for interpreting Bayes factors

2021 ◽  
Author(s):  
John K. Kruschke
Keyword(s):  
Posterior Distribution ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayes Factor ◽  
Bayes Factors ◽  
Prior Model ◽  
Bayesian Hypothesis Testing ◽  
Bayesian Model Comparison ◽  
Posterior Model Probability ◽  
Model Probability

In most applications of Bayesian model comparison or Bayesian hypothesis testing, the results are reported in terms of the Bayes factor only, not in terms of the posterior probabilities of the models. Posterior model probabilities are not reported because researchers are reluctant to declare prior model probabilities, which in turn stems from uncertainty in the prior. Fortunately, Bayesian formalisms are designed to embrace prior uncertainty, not ignore it. This article provides a novel derivation of the posterior distribution of model probability, and shows many examples. The posterior distribution is useful for making decisions taking into account the uncertainty of the posterior model probability. Benchmark Bayes factors are provided for a spectrum of priors on model probability. R code is posted at https://osf.io/36527/. This framework and tools will improve interpretation and usefulness of Bayes factors in all their applications.

scholarly journals Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8452
Author(s):  
Sofia Ruiz-Suarez ◽  
Vianey Leos-Barajas ◽  
Ignacio Alvarez-Castro ◽  
Juan Manuel Morales
Keyword(s):  
State Space ◽  
Continuous Time ◽  
Time Scale ◽  
Animal Movement ◽  
State Space Model ◽  
Movement Direction ◽  
Time Intervals ◽  
Space Model ◽  
Regular Time ◽  
Approximate Bayesian

The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.

scholarly journals Robust model selection for a semimartingale continuous time regression from discrete data

2015 ◽  
Vol 125 (1) ◽  
pp. 294-326 ◽  
Author(s):  
Konev Victor ◽  
Pergamenchtchikov Serguei
Keyword(s):  
Model Selection ◽  
Continuous Time ◽  
Discrete Data ◽  
Robust Model ◽  
Robust Model Selection ◽  
Selection For

scholarly journals Modeling interdependent animal movement in continuous time

Biometrics ◽  
2016 ◽  
Vol 72 (2) ◽  
pp. 315-324 ◽  
Author(s):  
Mu Niu ◽  
Paul G. Blackwell ◽  
Anna Skarin
Keyword(s):  
Continuous Time ◽  
Animal Movement

scholarly journals Bayesian model discrimination for partially-observed epidemic models

2019 ◽  
Author(s):  
James N. Walker ◽  
Andrew J. Black ◽  
Joshua V. Ross
Keyword(s):  
Model Selection ◽  
Importance Sampling ◽  
Continuous Time ◽  
Bayesian Model ◽  
Broad Class ◽  
Infectious Period ◽  
Importance Sampling Method ◽  
Markov Chain Models ◽  
Wide Range ◽  
Partially Observed

AbstractAn efficient method for Bayesian model selection is presented for a broad class of continuous-time Markov chain models and is subsequently applied to two important problems in epidemiology. The first problem is to identify the shape of the infectious period distribution; the second problem is to determine whether individuals display symptoms before, at the same time, or after they become infectious. In both cases we show that the correct model can be identified, in the majority of cases, from symptom onset data generated from multiple outbreaks in small populations. The method works by evaluating the likelihood using a particle filter that incorporates a novel importance sampling algorithm designed for partially-observed continuous-time Markov chains. This is combined with another importance sampling method to unbiasedly estimate the model evidence. These come with estimates of precision, which allow for stopping criterion to be employed. Our method is general and can be applied to a wide range of model selection problems in biological and epidemiological systems with intractable likelihood functions.

scholarly journals Continuous-time discrete-space models for animal movement

2015 ◽  
Vol 9 (1) ◽  
pp. 145-165 ◽  
Author(s):  
Ephraim M. Hanks ◽  
Mevin B. Hooten ◽  
Mat W. Alldredge
Keyword(s):  
Continuous Time ◽  
Animal Movement ◽  
Discrete Space

scholarly journals ExactBayesian inference for animal movement in continuous time

2015 ◽  
Vol 7 (2) ◽  
pp. 184-195 ◽  
Author(s):  
Paul G. Blackwell ◽  
Mu Niu ◽  
Mark S. Lambert ◽  
Scott D. LaPoint
Keyword(s):  
Continuous Time ◽  
Animal Movement

Fitting Dynamic Models to Animal Movement Data: The Importance of Probes for Model Selection, a Reply to Franz and Caillaud

2010 ◽  
Vol 175 (6) ◽  
pp. 762-764 ◽  
Author(s):  
Benjamin D. Dalziel ◽  
Juan M. Morales ◽  
John M. Fryxell
Keyword(s):  
Model Selection ◽  
Dynamic Models ◽  
Animal Movement ◽  
Movement Data

scholarly journals Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird

2018 ◽  
Vol 285 (1883) ◽  
pp. 20180788 ◽  
Author(s):  
Gemma Carroll ◽  
Robert Harcourt ◽  
Benjamin J. Pitcher ◽  
David Slip ◽  
Ian Jonsen
Keyword(s):  
Habitat Quality ◽  
Site Fidelity ◽  
Prey Capture ◽  
Dynamic Environment ◽  
Adaptive Strategy ◽  
Short Term ◽  
Foraging Site ◽  
Dynamic Habitat ◽  
Combine Information ◽  
Lower Site

Foraging site fidelity allows animals to increase their efficiency by returning to profitable feeding areas. However, the mechanisms underpinning why animals ‘stay’ or ‘switch’ sites have rarely been investigated. Here, we explore how habitat quality and prior prey capture experience influence short-term site fidelity by the little penguin ( Eudyptula minor ). Using 88 consecutive foraging trips by 20 brooding penguins, we found that site fidelity was higher after foraging trips where environmental conditions were favourable, and after trips where prey capture success was high. When penguins exhibited lower site fidelity, the number of prey captures relative to the previous trip increased, suggesting that switches in foraging location were an adaptive strategy in response to low prey capture rates. Penguins foraged closer to where other penguins foraged on the same day than they did to the location of their own previous foraging site, and caught more prey when they foraged close together. This suggests that penguins aggregated flexibly when prey was abundant and accessible. Our results illustrate how foraging predators can integrate information about prior experience with contemporary information such as social cues. This gives insight into how animals combine information adaptively to exploit changing prey distribution in a dynamic environment.

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