Abiotic heterogeneity underlies trait‐based competition and assembly
Bibliographic record
Abstract
Abstract The fitness of individual species depends on their ability to persist and establish at low densities, just as the diversity of ecological communities depends on the establishment and persistence of low‐density, “invader” species. Theory predicts that abiotic conditions and the competitive make‐up of resident communities jointly shape invader fitness, limiting the phenotypic identity of successful invaders. We use an invasion experiment to ask how competitive traits of 20 introduced plant species alter their absolute fitness in fragments that differ in size, abiotic conditions, and traits of the resident community. We show that abiotic conditions interact with both invader traits and resident functional diversity to determine invader survival. Optimal invader traits depended on the soil characteristics, while greater resident trait diversity lowered invader fitness and had especially strong effects in low‐resource environments. Unlike other abiotic conditions, fragment size had consistent effects irrespective of invader identity, decreasing survival in larger fragments. Synthesis. Our results illustrate how the abiotic environment mediates the effects of resident and invader traits on establishment, creating fitness landscapes that structure local diversity and the functional identities of successful species.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".