  Point counts (PCs) are widely used in biodiversity surveys, but despite
numerous advantages, simple PCs suffer from several problems: detectability,
and therefore abundance, is unknown; systematic spatiotemporal variation in
detectability produces biased inferences, and unknown survey area prevents
formal density estimation and scaling-up to the landscape level. We introduce
integrated distance sampling (IDS) models that combine distance sampling (DS)
with simple PC or detection/nondetection (DND) data and capitalize on the
strengths and mitigate the weaknesses of each data type. Key to IDS models is
the view of simple PC and DND data as aggregations of latent DS surveys that
observe the same underlying density process. This enables estimation of
separate detection functions, along with distinct covariate effects, for all
data types. Additional information from repeat or time-removal surveys, or
variable survey duration, enables separate estimation of the availability and
perceptibility components of detectability. IDS models reconcile spatial and
temporal mismatches among data sets and solve the above-mentioned problems of
simple PC and DND data. To fit IDS models, we provide JAGS code and the new
IDS() function in the R package unmarked. Extant citizen-science data generally
lack adjustments for detection biases, but IDS models address this shortcoming,
thus greatly extending the utility and reach of these data. In addition, they
enable formal density estimation in hybrid designs, which efficiently combine
distance sampling with distance-free, point-based PC or DND surveys. We believe
that IDS models have considerable scope in ecology, management, and monitoring.
