Bridging general and targeted monitoring to reduce detectability bias in population indicators in the Common Quail

Francesc Sardà-Palomera, Manel Puigcerver, Irene Peña de la Cruz, Marc Anton, José Domingo Rodríguez Teijeiro

Breeding Bird Monitoring Schemes (BMS), a cornerstone of large-scale volunteer-based ecological monitoring, are central to biodiversity assessment and conservation decision-making. However, their generalist design means that detectability can vary across species, habitats and behavioral states, introducing noise into abundance estimates and population indices. Improving how BMS account for detectability-related bias is therefore essential for strengthening their applied value in conservation and management, particularly when population indicators are used to guide policy, prioritization and adaptive management actions. Here, we develop and test a calibration framework to adjust for detectability-related bias in BMS counts by integrating information from a targeted, species-specific high-detection survey conducted in parallel. Using the Common Quail (Coturnix coturnix), a farmland species whose irregular and density-dependent calling behavior generates strong variation in detectability, we quantified differences in detection, abundance estimates and temporal trends between the two monitoring approaches. The targeted survey detected quails in 32% of surveys classified as absences by the BMS method, revealing substantial detectability mismatches. When detections occurred under both methods, the targeted survey recorded more than twice as many individuals per survey, indicating marked bias in local abundance estimates under general monitoring. We then fitted a habitat-informed calibration model that adjusts BMS counts using vegetation greenness (NDVI) as a proxy of habitat quality. Discrepancies between methods were largest in high-quality habitats and under low BMS counts. Applying the calibration reduced noise associated with detectability variability and improved the reliability of BMS-derived trend indices. By explicitly addressing detectability-related bias, this approach provides an operational and transferable framework for improving monitoring-based indicators used in conservation assessment and management. More broadly, it illustrates how integrating targeted, high-detection surveys with broad-scale volunteer-based monitoring can enhance the decision relevance of biodiversity monitoring programs without compromising their scalability or long-term continuity.