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

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Breeding Bird Monitoring Schemes (BMS) provide large-scale, long-term data essential for biodiversity assessment and conservation decision-making. However, their multispecies design can generate species-specific detectability biases, particularly for taxa whose behavioral or ecological traits deviate from standardized count assumptions, potentially affecting abundance estimates and population indicators. We propose a structured integration framework linking general monitoring schemes with species-specific high-detection surveys through parallel designs and ecological modeling. The approach involves (1) implementing temporally matched general and targeted surveys, (2) modeling the relationship between survey outputs while incorporating ecological covariates influencing detectability differences, and (3) projecting the calibrated relationship onto broader BMS datasets to improve abundance indices and trend estimation. We apply this framework to the Common Quail (Coturnix coturnix), a farmland game species with context-dependent detectability under standardized protocols. Parallel surveys revealed marked discrepancies between monitoring methods across habitat conditions. Incorporating habitat information into the calibration reduced detectability-related variability and improved the consistency of long-term trend estimates. By formally linking general and targeted systems, this framework enhances the reliability and policy relevance of biodiversity indicators while retaining the spatial and temporal scalability of existing monitoring programs. https://doi.org/10.32942/X2R942 Life Sciences abundance estimation, biodiversity assessment, long-term monitoring, population trends, sampling bias, species-specific surveys, volunteer-based monitoring Published: 2026-02-04 02:26 Last Updated: 2026-02-27 02:14 CC BY Attribution 4.0 International Data and Code Availability Statement: Open data are not available due to data ownership and sensitivity considerations associated with long-term monitoring programmes. No novel code was developed for this study. Language: English