Estimating regional species richness: The case of China's vascular plant species
Bibliographic record
Abstract
Abstract Aim The estimation of regional species richness has been a major challenge in ecology but is crucial for setting up conservation priorities. Discovery curves are a principal tool for estimating regional richness, but they have been criticized for being too sensitive to historical fluctuations in species discovery. In this study we propose a new discovery model that considers historical influences. We have applied the model to estimate the number of vascular plant species in China, a country of mega‐biodiversity that has also suffered endless wars and social turmoil in its modern history. Location China. Time period 1755–2000. Major taxa studied Vascular plant species. Methods We compiled the discovery time for each vascular species from the complete volumes ofFlora of China, leading to 31,220 valid species names. We applied our model to these data and compared the performance of our model with existing discovery models and two other methods (species–area relationships and a taxonomic rank curve model). We also tested our model with three other independent datasets and one simulation study. Results Our new method estimated there to be 36,554 (± 2,708) vascular plant species in China. Our model accounted very well for the effect of historical events and was robust to different periods of data that were used to estimate the total richness. Our model outperformed all other models that were compared. We found that 5,334 species remained to be discovered in China and it would take about 50 years to discover all the species should the current discovery rate of 110 species per year persist. Main conclusions Species discovery curves, with historical effects being properly accounted for, offer a promising tool for estimating regional species richness. Our model is robust to the effect of historical events and provides by far the most accurate and reliable estimates of species richness of test data, including vascular plant richness in China.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".