Assessing non‐parametric and area‐based methods for estimating regional species richness
Bibliographic record
Abstract
Abstract Questions Many methods have been developed to estimate species richness but few are useful for estimating regional richness. We compared the performance of commonly used non‐parametric and area‐based estimators with a particular focus on testing a newly developed but little tested maximum entropy method (MaxEnt). Location Tropical forest of Jianfengling Reserve, Hainan Island, China. Methods We extrapolated species richness on 12 estimators up to a larger regional scale – the reserve (472 km 2 ) – where 164 25 m × 25 m quadrats were distributed on a grid of 160 km 2 within the tropical forest. We also analysed the effects of base (or ‘anchor‘) scale A 0 on the species richness estimated ( S est ) with MaxEnt. Results Six non‐parametric methods underestimated the species richness, while six area‐based methods overestimated the species richness. The accuracy of the MaxEnt estimate ( S est ) was improved with the increase of base scale A 0 . Conclusions Our findings suggest non‐parametric methods should not be used to estimate richness across heterogeneous landscapes but can be used in well‐defined sampling areas. Jack2 is the best of the six non‐parametric methods, while the logistic model and the MaxEnt method seem to be the best of the six area‐based methods. Improvements to the MaxEnt method are possible but that will require reformulation of the method by considering species–abundance distributions other than log‐series and more general spatial allocation rules.
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 | 0.040 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".