ESTIMATING SPECIES RICHNESS OF TROPICAL BIRD COMMUNITIES FROM RAPID ASSESSMENT DATA
Bibliographic record
Abstract
Rapid assessment surveys of tropical bird communities are increasingly used to estimate species richness and to determine conservation priorities, but results of different studies are often not comparable due to the lack of standardization. On the basis of computer simulations and six years of field testing, we evaluated the recently proposed “20-species-list” survey method and statistical estimators for assessing species richness of tropical bird communities. This method generates a species-accumulation curve by subdividing consecutive observations of birds into lists of 20 species, thus relating cumulative species richness to the number of observations rather than time or space and thereby accounting for moderate differences in observer qualification and field conditions. Species accumulation curves from computer-simulated communities and two empirical data sets from Bolivia were analyzed with nine species richness estimators to evaluate estimator accuracy with respect to variations in species-list size, sample size, species-pool size, and community structure. For empirical and most simulated data sets, the MMMEAN estimator performed best, but it was more sensitive to differences in community structure than most other estimators. The CHAO 2 estimator, which was recommended by previous studies, performed reasonably well but was considerably more sensitive to sample size than MMMEAN. The bootstrap and first- and second-order jackknife estimators performed poorly. We recommend using MMMEAN or, when standard deviations of richness estimates are indispensable, CHAO 2 with 10-species lists for estimating species richness of tropical bird communities and propose a set of standard survey rules. Careful examination of estimator accumulation curves is required, however, and a technique based on the ratio between estimator and species accumulation curve is suggested to control for the confounding effects of sampling effort. Overall, the species-list method combined with statistical richness estimation is doubtlessly much more standardized and valuable than simple comparisons of one-dimensional locality lists and represents a promising tool for conservation assessment and the study of avian diversity patterns in the tropics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".