Disturbance and resource availability act differently on the same suite of plant traits: revisiting assembly hypotheses
Bibliographic record
Abstract
Understanding the mechanisms of trait selection at the scale of plant communities is a crucial step toward predicting community assembly. Although it is commonly assumed that disturbance and resource availability constrain separate suites of traits, representing the regenerative and established phases, respectively, a quantification and test of this accepted hypothesis is still lacking due to limitations of traditional statistical techniques. In this paper we quantify, using structural equation modeling (SEM), the relative contributions of disturbance and resource availability to the selection of suites of traits at the community scale. Our model specifies and reflects previously obtained ecological insights, taking disturbance and nutrient availability as central drivers affecting leaf, allometric, seed, and phenology traits in 156 (semi-) natural plant communities throughout The Netherlands. The common hypothesis positing that disturbance and resource availability each affect a set of mutually independent traits was not consistent with the data. Instead, our final model shows that most traits are strongly affected by both drivers. In addition, trait-trait constraints are more important in community assembly than environmental drivers in half of the cases. Both aspects of trait selection are crucial for correctly predicting ecosystem processes and community assembly, and they provide new insights into hitherto underappreciated ecological interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".