Demographic drivers of functional composition dynamics
Bibliographic record
Abstract
Mechanisms of community assembly and ecosystem function are often analyzed using community-weighted mean trait values (CWMs). We present a novel conceptual framework to quantify the contribution of demographic processes (i.e., growth, recruitment, and mortality) to temporal changes in CWMs. We used this framework to analyze mechanisms of secondary succession in wet tropical forests in Mexico. Seed size increased over time, reflecting a trade-off between colonization by small seeds early in succession, to establishment by large seeds later in succession. Specific leaf area (SLA) and leaf phosphorus content decreased over time, reflecting a trade-off between fast growth early in succession vs. high survival late in succession. On average, CWM shifts were driven mainly (70%) by growth of surviving trees that comprise the bulk of standing biomass, then mortality (25%), and weakly by recruitment (5%). Trait shifts of growing and recruiting trees mirrored the CWM trait shifts, and traits of dying trees did not change during succession, indicating that these traits are important for recruitment and growth, but not for mortality, during the first 30 yr of succession. Identifying the demographic drivers of functional composition change links population dynamics to community change, and enhances insights into mechanisms of succession.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".