Integrating species-specific and volunteer-based monitoring to improve population indicators: lessons from the Common Quail

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

Breeding Bird Monitoring Schemes (BMS) provide the backbone of large-scale biodiversity assessment and are widely used to inform conservation policy and management. However, for species with low or variable detectability, abundance-based indicators derived from general monitoring schemes may not accurately reflect population dynamics. This issue is particularly relevant for species with highly variable detectability. Using the Common Quail (Coturnix coturnix), a widespread farmland game bird, we evaluated how detectability bias influences abundance estimates and long-term population indicators derived from volunteer-based monitoring.

We compared data from a general breeding bird monitoring scheme and a species-specific survey designed to maximize quail detectability. The species-specific survey frequently detected quails in surveys classified as absences by the general monitoring scheme and consistently recorded higher abundances when both methods detected the species. A calibration model incorporating vegetation greenness (NDVI) translated general survey counts into species-specific abundance indicators and showed that differences between monitoring approaches depended on habitat conditions.

Although overall long-term trends were broadly similar between monitoring approaches, substantial differences emerged in high-quality habitats, where detectability bias was strongest and uncorrected indicators suggested population declines that were not supported by calibrated estimates. These results indicate that detectability bias can influence population trend indicators and potentially affect conservation assessments. Integrating species-specific and volunteer-based monitoring provides a practical framework for improving biodiversity indicators without sacrificing the spatial and temporal coverage of large-scale monitoring programs.