A link between plant traits and abundance: evidence from coastal California woody plants
Bibliographic record
Abstract
Summary 1. A number of recent studies have demonstrated that plant traits play a crucial role in determining the success or failure of species in a given environment. However, whether traits play a role in determining species’ abundance and rarity among the co‐occurring species within a community remains an unresolved question. 2. To address this, we analysed the abundance of California coastal woody plant species at landscape and local scales in relation to 11 leaf, wood and seed traits. 3. At the landscape scale, we found no significant relationship between traits and abundance. In contrast, at the local scale we found significant relationships between abundance and four traits: specific leaf area (SLA), height, lumen fraction and wood density. For SLA and height, the relationship was linear; for lumen fraction, it was quadratic. For wood density, the direction of the trait–abundance relationship was dependant on the abiotic context, that is, it shifted across a gradient in soil water content. 4. Synthesis. Understanding the connections between traits and abundance is important for two reasons. First, there is an ongoing debate about the degree to which commonness and rarity are the result of drift among ecologically equivalent species or niche processes. These results suggest that there are non‐random, trait‐based processes affecting abundance and rarity. Secondly, species’ traits have been shown to have a strong effect on photosynthesis and decomposition rates, mediated by the abundance of the species. The connections between traits and abundance presented here are crucial for scaling from measurements of species’ traits to ecosystem‐level processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".