Single bin QPAD (SQPAD) approach for robust analysis of point-count data with detection error

Abstract Bird population monitoring is often conducted using point-count surveys. Accounting for detection errors is a major challenge in analyzing these data. The commonly used methods for correcting detection errors in counts of organisms are distance sampling, removal sampling, N-mixture, and QPAD. These methods rely on multiple surveys or subdivisions within surveys (time/distance bins). The reliability of these approaches depends on the accurate estimation of distance, correct identification of individuals, and the closed population assumption. Errors in distance estimation, double counting, and mortality and migration of individuals within and between survey periods can lead to substantial biases in population density estimation. Furthermore, tracking individuals and estimating distances can be difficult in field conditions. We propose a simple modification of the QPAD method so that field observers are required to collect information only about either the occupancy status or count of individuals within a specified time interval and a specified spatial buffer, a “single bin,” around the observer’s location. We show that population density parameters are identifiable by changing the time interval and the radius of the spatial buffer for each survey location. We show that this variable-effort survey method is robust against errors in distance estimation and double counting. We also show that data collected under current protocols in North America can be analyzed using single bin QPAD. We illustrate our methodology with biologically realistic simulations and a reanalysis of some field data.