Integrated population models provide a framework for assimilating multiple datasets to understand population dynamics. Understanding drivers of demography is key to improving wildlife management, and integrated population models have informed conservation practices for many species of conservation concern. Motivated by multiple surveys of lesser prairie-chicken (Tympanuchus pallidicinctus), we developed a flexible integrated population modeling framework for assimilating demographic data with multiple surveys of abundance. Measurements of abundance are derived from aerial and ground surveys that vary in their observational uncertainty, sampling design, temporal coverage, and survey effort. Our proposed integrated population model draws from the strengths of each survey and prevents their sampling biases from compromising inference. We facilitate posterior inference for our integrated population model using chained Markov melding, which induces the joint distribution for all data sources by linking inference across several submodels. Using Markov melding, we extend the modeling framework previously proposed for analyzing the individual data sources while still obtaining joint Bayesian inference. We fit the melded model with a multistage Markov chain Monte Carlo algorithm that decreases run time and improves mixing. We assimilate data from several state and federal wildlife agencies and over a dozen independent researchers to infer lesser prairie-chicken abundance and vital rates across its entire range over the last 18 years. Supplementary materials accompanying this paper appear online.