  Detection heterogeneity is inherent to ecological data, arising from factors
such as varied terrain or weather conditions, inconsistent sampling effort, or
heterogeneity of individuals themselves. Incorporating additional covariates
into a statistical model is one approach for addressing heterogeneity, but is
no guarantee that any set of measurable covariates will adequately address the
heterogeneity, and the presence of unmodelled heterogeneity has been shown to
produce biases in the resulting inferences. Other approaches for addressing
heterogeneity include the use of random effects, or finite mixtures of
homogeneous subgroups. Here, we present a non-parametric approach for modelling
detection heterogeneity for use in a Bayesian hierarchical framework. We employ
a Dirichlet process mixture which allows a flexible number of population
subgroups without the need to pre-specify this number of subgroups as in a
finite mixture. We describe this non-parametric approach, then consider its use
for modelling detection heterogeneity in two common ecological motifs:
capture-recapture and occupancy modelling. For each, we consider a homogeneous
model, finite mixture models, and the non-parametric approach. We compare these
approaches using two simulation studies, and observe the non-parametric
approach as the most reliable method for addressing varying degrees of
heterogeneity. We also present two real-data examples, and compare the
inferences resulting from each modelling approach. Analyses are carried out
using the \texttt{nimble} package for \texttt{R}, which provides facilities for
Bayesian non-parametric models.
