Species Richness and the Temporal Stability of Biomass Production: A New Analysis of Recent Biodiversity Experiments
Bibliographic record
Abstract
The relationship between biological diversity and ecological stability has fascinated ecologists for decades. Determining the generality of this relationship, and discovering the mechanisms that underlie it, are vitally important for ecosystem management. Here, we investigate how species richness affects the temporal stability of biomass production by reanalyzing 27 recent biodiversity experiments conducted with primary producers. We find that, in grasslands, increasing species richness stabilizes whole-community biomass but destabilizes the dynamics of constituent populations. Community biomass is stabilized because species richness impacts mean biomass more strongly than its variance. In algal communities, species richness has a minimal effect on community stability because richness affects the mean and variance of biomass nearly equally. Using a new measure of synchrony among species, we find that for both grasslands and algae, temporal correlations in species biomass are lower when species are grown together in polyculture than when grown alone in monoculture. These results suggest that interspecific interactions tend to stabilize community biomass in diverse communities. Contrary to prevailing theory, we found no evidence that species' responses to environmental variation in monoculture predicted the strength of diversity's stabilizing effect. Together, these results deepen our understanding of when and why increasing species richness stabilizes community biomass.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".