Codes for simulation and data for: The relationship between local and regional extinction rates depends on species distribution patterns
Bibliographic record
Abstract
The rapid loss of biodiversity poses a great threat to ecosystem functions and services. Credible estimation of species extinction rates is essential for understanding the magnitude of biodiversity loss and for informing conservation, but this has been a challenge because estimated extinctions are unverifiable due to the lack of data. In this study, we investigated the relationship between local and regional extinctions and assessed the effects of range size, spatial segregation, and patchiness of species distribution on this local-regional extinction relationship. We found that regional extinction rates had a convex relationship with local extinction rates, that is, the regional extinction rate was most likely to be lower than the average local rate. The regional rates deviated from local rates as the sampling area decreased. The difference between local and regional extinction rates (local-regional extinction difference) became larger if a higher number of species had larger range sizes and patchiness. We also detected that there were interactive effects among these factors. Species segregation had a weak positive relationship with the local-regional extinction difference if more species had relatively large range sizes. As the sampling areas increased, the range size showed smaller positive effects on local-regional differences, but patchiness showed larger positive effects. The local-regional extinction relationship of this study provides insights into the spatial scaling of biodiversity loss and offers some important cues for estimating regional extinctions from local data in future studies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.121 | 0.076 |
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".