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

marine mammal laboratory, Alaska Fisheries Science Center, Noaa National Marine Fisheries Service, Seattle, U.S.A

marine mammal laboratory, Alaska Fisheries Science Center, Noaa National Marine Fisheries Service, Seattle, U.S.A

This article was accepted for publication and subjected complete peer reviews, but has not performed by the copediting, cereal, pagination and correction control process, which can lead to differences between this version and the version of the record. Please quote this article as DOI: 10.1111 / x.13619

Abstract

1. Hidden Markov models (HMMS) with which the random effects of the individual level are included recently promoted animal movement behavior from biotelematrics data. These "mixed HMMs" are considerable costs in terms of implementation and calculation, and discrete random effects were granted as a practical alternative to computational continuous random effects. However, the performance of mixed HMMs is not yet sufficiently explored to justify its widespread adoption, and there is currently little guideline for the costs and the benefits of mixed HMMs for a specific research goal.

2. I conducted a comprehensive simulation study comparing the performance of a series of solid and random effects models for individual heterogeneity in the hidden state process of a 2-state HMMS. I focused on the typical scenarios for telemetry studies, which often consist of relatively long time series ( observations per animal) for relatively few people ( animals).

3. I've usually found mixed HMMs that did not improve the status order with respect to standard HMMS. The reliable estimation of the random effects required larger pattern sizes than in telemetry studies often feasible. Continuous random effects models led to a reasonably well-random effects, but not vice versa. Random effects that take into account inexplicable individual variations can improve the estimation of the state transitional probabilities and measurable covariate effects, but discrete random effects can be a relatively poor (and possibly misleading) approach for continuous variations.

4. When weighing the costs and the advantages of mixed HMMs, three important considerations are to study target destinations, pattern size and model complexity. HMM applications often focus on the state task with low emphasis on heterogeneity in the state transitional probabilities, with accidental effects in the hidden state operation simply not be worth the additional effort. However, if a variation of the state transitional probabilities will be explained, a main objective and sufficient explanatory covariates are not available, then random effects are to be pursued as a submitting alternative to individual solid effects.

5. To help use my insights into the context and to illustrate some potential challenges that can encounter practitioners in the application of mixed HMMS, I visit a previous analysis of long fine pilot-whale biotelematrics data.

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