Integrated models are a popular tool for analyzing species of conservation
concern. Species of conservation concern are often monitored by multiple
entities that generate several datasets. Individually, these datasets may be
insufficient for guiding management due to low spatio-temporal resolution,
biased sampling, or large observational uncertainty. Integrated models provide
an approach for assimilating multiple datasets in a coherent framework that can
compensate for these deficiencies. While conventional integrated models have
been used to assimilate count data with surveys of survival, fecundity, and
harvest, they can also assimilate ecological surveys that have differing
spatio-temporal regions and observational uncertainties. Motivated by
independent aerial and ground surveys of lesser prairie-chicken abundance, we
developed an integrated modeling approach that assimilates density estimates
derived from surveys with distinct sources of observational error into a joint
framework that provides shared inference on spatio-temporal trends. For
implementation, we model these data using a Bayesian Markov melding approach
and apply several data augmentation strategies for efficient sampling. Our
integrated model decreased uncertainty in annual density estimates, facilitated
prediction at unsampled regions, and quantified the inferential cost associated
with reduced survey effort.Comment: 22 pages; 5 figures, 1 table, submitted to Biometric