Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".