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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".