  To study population dynamics, ecologists and wildlife biologists use relative
abundance data, which are often subject to temporal preferential sampling.
Temporal preferential sampling occurs when sampling effort varies across time.
To account for preferential sampling, we specify a Bayesian hierarchical
abundance model that considers the dependence between observation times and the
ecological process of interest. The proposed model improves abundance estimates
during periods of infrequent observation and accounts for temporal preferential
sampling in discrete time. Additionally, our model facilitates posterior
inference for population growth rates and mechanistic phenometrics. We apply
our model to analyze both simulated data and mosquito count data collected by
the National Ecological Observatory Network. In the second case study, we
characterize the population growth rate and abundance of several mosquito
species in the Aedes genus.
