An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence.

Understanding population dynamics requires accurate estimates of demographic rates. Integrated population models combine demographic and survey data into a single, comprehensive analysis and provide more coherent estimates of vital rates. Integrated population models rely on the assumption that different data sets are independent, which is frequently violated in practice. Moreover, the precision that can be gained using integrated modeling compared to conventional modeling is only known from empirical studies. The present study used simulation methods to assess how the violation of the assumption of independence affects the statistical properties of the parameter estimators. Further, the gains in precision and accuracy from the model were explored under varying sample sizes. For capture-recapture, population survey, and reproductive success, we generated independent and dependent data that were analyzed with integrated and conventional models. We found only a minimal impact of the violation of the assumption of independence on the parameter estimates. Furthermore, we observed an overall gain in precision and accuracy when all three data sets were analyzed simultaneously. This was particularly pronounced when the sample size was small. These findings contribute to clearing the way for the application of integrated population models in practice.