Capture-recapture techniques for wildlife provide valuable information but are often more cost-prohibitive at large spatial and temporal scales than less-intensive sampling techniques. Model development combining multiple data sources to leverage data source strengths and for improved parameter precision has increased, but with limited discussion on precision gain versus effort. Some data sources take more effort than others, thus knowing how much improvement is gained with metrics selected for monitoring objectives is important for efficiently allocating samples on the landscape with limited budgets.
We present a general framework for evaluating trade-offs between improvements in metrics selected for monitoring objectives and costs associated with acquiring multiple data sources. This framework is useful for designing future or new phases of current studies. We illustrated how joint data models incorporating detection/non-detection (distributed across the landscape) and banding (subset of locations within landscape) data can improve abundance, survival, and recruitment estimates, and quantified data source costs in a northern Arizona, USA western bluebird population. This 8-year data subset is part of a larger project evaluating wildfire effects of bird communities in ponderosa pine forests. We constructed separate models using these two data sources, and a joint model using both data types to evaluate improvements in metrics relative to effort. Joint model metrics were more precise than single data model estimates, but parameter precision varied depending on the metric of interest. Justification of increased costs associated with additional data types depends on project objectives.