  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, while HMMs are routinely applied in other disciplines,
they have only recently begun to gain traction within the broader ecological
community. We provide a gentle introduction to HMMs, establish some common
terminology, and review the immense scope of HMMs for applied ecological
research. We also provide a supplemental tutorial on some of the more technical
aspects of HMM 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.
