What drives plant species diversity? A global distributed test of the unimodal relationship between herbaceous species richness and plant biomass
Bibliographic record
Abstract
Abstract Question For over a century, ecologists have grappled with the question “what drives species diversity?” Urgent global issues such as loss of biodiversity and the relative importance of species richness for ecosystem function and services has heightened the relative importance of understanding processes that control species diversity. Here we present the plans for a global coordinated distributed experiment for herbaceous communities, theHerbDivNet, to test the hump‐backed model, a unimodal relationship between species richness and aboveground plant biomass plus dead plant litterHBM, to determine whether scale may influence theHBM, and to explore drivers of plant diversity. Location Globally distributed experiment. Methods We propose a nested, standardized sampling design 8 × 8 m, with 1 m2plots, taken from multiple site locations along a range of sites varying in primary productivity. Results and Conclusions We welcome others with an interest in using global, standardized, coordinated distributed experiments to explore patterns and processes in herbaceous plant communities to joinHerbDivNet in the search of new insights to drivers of plant species diversity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".