Community‐level consequences of species interactions in an annual plant community
Bibliographic record
Abstract
Abstract Question: How does the intensity of species interactions affect species and functional group composition of an annual plant community? Location: Sede Boqer, Negev Desert, Israel. Methods: The potential for competitive interactions in two annual plant communities (desert and coastal) from semi‐stabilized sand dunes was manipulated by varying seed bank density and therefore the number of potentially interacting individuals. Communities were exposed to three different irrigation regimes, mimicking precipitation at the desert site, the coastal site, and an intermediate precipitation level. Plots were maintained for 3 years, and percentage cover of each species in the plots was recorded at the end of each growing season. We used redundancy analysis to test for effects of initial density, irrigation, and year on the species and functional group composition of the communities. Results: Initial density had significant effects on species composition, and these effects remained significant over 3 years, even as total community percentage cover became more similar among treatments over time. Density effects did not depend on resource availability (irrigation level). Functional group identity or individual plant size did not predict which species would be good competitors, and a species' competitive ability did not predict its abundance in the field. Conclusions: Species interactions strongly affect community composition, and those effects carry over into subsequent years such that competition does not lead to convergence in community structure over time. However, the particular changes in composition observed were not predictable by some of the traits that have been found important in individual‐level experiments. We speculate that the outcome of competition in diverse communities will depend on multiple traits, in contrast to the outcome of individual‐level pairwise experiments. We also speculate that the shift in composition with density could mean that local variation in density may contribute to maintenance of diversity in this system.
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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 | 0.000 | 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 | 0.000 |
| 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 | 0.000 |
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".