Density regulation in annual plant communities under variable resource levels
Bibliographic record
Abstract
Density regulation is assumed to be common, but is very rarely tested experimentally. Using annual plant communities, we tested the hypotheses that 1) regulation of abundance in plants occurs at the level of entire communities, not just within species, and 2) such regulation is strongest when resources are most limiting. We transplanted different amounts of seeds from two diverse source communities in Israel to an experimental garden and monitored plant densities and cover for two years under different irrigation regimes. Both total density and total plant cover showed strong evidence of community‐level regulation; plots sown at higher than average natural density declined or stayed the same in total abundance over time, while plots sown at lower than average natural density increased in abundance over time. This convergence of community abundance was strongest with the lowest irrigation in both source communities, consistent with the hypothesis of stronger regulation when all resources are more limiting (light levels were high, regardless of irrigation level). The main mechanism of regulation was strong density dependent recruitment, while survival was either density independent or inversely density dependent. Thus, the results also emphasize the need for direct experimental studies of the population dynamic consequences of interactions, as well as of individual level consequences.
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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 | 0.001 |
| 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 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".