Imperfect detection biases β diversity estimates based on whether species are shared between sites

Imperfect detection is a form of sampling error that can bias measurements of species occurrence and is widely known to bias measures of local (α) and regional (γ) diversity. However, it is less well known how imperfect detection affects estimates of β diversity, the variation in species composition among sites, especially when incorporating species traits and evolutionary histories. Using a decade of avian monitoring data collected across 36 subtropical islands, occupancy model was applied to correct imperfect detection and to quantify its impact on taxonomic, functional, and phylogenetic β diversity in relation to island area and isolation. To assess the broader generality of these patterns, simulations were conducted incorporating multiple ecological and sampling drivers, including survey design and community structure. Both empirical and simulated analyses revealed that imperfect detection consistently led to overestimates of taxonomic, functional, and phylogenetic β diversity, primarily due to the under-detection of shared species, which, in turn, obscured diversity relationships with island attributes. In the empirical dataset, the extent of overestimation increased with greater differences in island area, whereas simulations demonstrated that repeated surveys per site effectively reduced this bias. Collectively, these findings establish a general framework explaining how imperfect detection systematically biases all facets of β diversity by altering observed species composition. This mechanism offers broad applicability across various biological taxa and ecological systems, enhancing the accuracy of biodiversity measurements, particularly functional and phylogenetic diversity. Given the importance of β diversity in understanding spatial and temporal community turnover, it is imperative to prioritize its accurate quantification.