Experimental evidence that evolutionarily diverse assemblages result in higher productivity
Bibliographic record
Abstract
There now is ample experimental evidence that speciose assemblages are more productive and provide a greater amount of ecosystem services than depauperate ones. However, these experiments often conclude that there is a higher probability of including complementary species combinations in assemblages with more species and lack a priori prediction about which species combinations maximize function. Here, I report the results of an experiment manipulating the evolutionary relatedness of constituent plant species across a richness gradient. I show that assemblages with distantly related species contributed most to the higher biomass production in multispecies assemblages, through species complementarity. Species produced more biomass than predicted from their monocultures when they were in plots with distantly related species and produced the amount of biomass predicted from monoculture when sown with close relatives. This finding suggests that in the absence of any other information, combining distantly related species in restored or managed landscapes may serve to maximize biomass production and carbon sequestration, thus merging calls to conserve evolutionary history and maximize ecosystem function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".