Ecosystem productivity can be predicted from potential relative growth rate and species abundance
Bibliographic record
Abstract
We show that ecosystem-specific aboveground net primary productivity (SANPP, g g(-1) day(-1), productivity on a per gram basis) can be predicted from species-level measures of potential relative growth rate (RGRmax), but only if RGRmax is weighted according to the species' relative abundance. This is in agreement with Grime's mass-ratio hypothesis. Productivity was measured in 12 sites in a French Mediterranean post-agricultural succession, while RGRmax was measured on 26 of the most abundant species from this successional sere, grown hydroponically. RGRmax was only weakly correlated (r2 = 0.12, P < 0.05) with field age when species abundance was not considered, but the two variables were strongly correlated (r2 = 0.81, P < 0.001) when the relative abundance of species in each field was taken into account. SANPP also decreased significantly with field age. This resulted in a tight relationship (r2 = 0.77, P < 0.001) between productivity and RGRmax weighted according to species relative biomass contribution. Our study shows that scaling-up from the potential properties of individual species is possible, and that information on potential and realized species traits can be integrated to predict ecosystem functioning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".