Uncovering ecological state dynamics with hidden Markov models

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.

Estimating the contribution of HIV-infected adults to household pneumococcal transmission in South Africa, 2016-2018: A hidden Markov modelling study

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

“…However, standard open population capture-recapture models are simply special cases of HMMs (e.g. Pradel 2005; McClintock et al 2020). Full technical details can be found in Burnham & White (2002), but I describe their approach in the context of individual random effects for the state transition probabilities (Eq.…”

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Toward Efficient Bayesian Approaches to Inference in Hierarchical Hidden Markov Models for Inferring Animal Behavior

“…In fact, HMMs have a great interpretive potential that allows to deal with unmeasured state processes and identify transitions in "hidden" states, even if such transitions are not evident from the observations (Tucker and Anand, 2005). By formally extricating state and observation processes based on manageable yet powerful mathematical properties, HMMs can be used to interpret many ecological phenomena, as they facilitate inferences about complex system state dynamics that would otherwise be intractable (McClintock et al, 2020). Thus, the success of applying HMMs in ecological systems lies in the combination of biological expertise with the use of sophisticated movement models as generating mechanisms for the observed data (Leos Barajas and Michelot, 2018).…”