Assessing the efficacy and cost‐effectiveness of integrating autonomous recording units and point‐count surveys for population monitoring of northern bobwhite ( <i>Colinus virginianus</i> )

Abstract Autonomous recording units (ARUs) have emerged as a useful tool for monitoring a wide range of acoustically signalling wildlife species in a cost‐effective manner, but estimating abundance from acoustic data can be challenging. Wildlife management efforts would benefit from a better understanding of how well ARUs can survey population abundance compared to traditional survey methods, as well as recommendations for the most cost‐effective monitoring strategies. We used empirical data and simulations of biologically realistic datasets to assess the relative benefits and costs associated with estimating abundance from ARU and point‐count data collected from northern bobwhite ( Colinus virginianus ), a species of cultural and commercial importance that has been declining across much of its range. Integrating ARUs and point counts increased the accuracy and precision of abundance estimates compared to estimates from each data set individually. The value of data integration was greatest at low survey effort and population density, as point counts alone often suffered from few detections and poor model performance under these circumstances. Integrating ARUs and point counts was generally the most cost‐effective monitoring strategy when density was &lt;1.7 birds/ha. Models with only ARU data performed well at very low abundance and survey effort, but performance declined as density increased. Practical Implication . Integrating ARUs and point counts can improve long‐term abundance monitoring for bobwhite, and likely for a wide range of other species. The relative benefits of ARUs versus point counts were most evident when surveying rare populations, though integration increased estimator precision across a range of population densities. Our results suggest that ARUs could be useful for reducing costs of point count monitoring efforts while maintaining similar levels of monitoring efficiency and could be a cost‐effective method for increasing the temporal or spatial scale of monitoring efforts.