Plant functional traits in Australian subtropical rain forest: partitioning within‐community from cross‐landscape variation
Bibliographic record
Abstract
Summary 1. Plant functional traits are dimensions of ecological strategy variation and provide insights into the assembly of plant communities. For woody rain forest vegetation of northern coastal New South Wales, Australia, we quantified four continuous traits (leaf size, seed size, wood density and maximum height) for 231 freestanding woody species and documented community composition for 216 plots. Using trait‐gradient analysis, we partitioned species trait values between alpha (within‐site) and beta (among‐site) components. This allowed us to identify both trait shifts along gradients and variation among co‐occurring species. 2. Alpha trait components consistently varied more widely than beta components, meaning that trait variation among species within plots was wider than variation in the mean trait values of plots where species typically grow. 3. Beta trait components covaried significantly among leaf area, seed size, wood density and maximum height. For example, species found in habitats with a large mean leaf size were consistently also found in plots with large mean seed size ( r = 0.70). Beta correlations show that these leaf, wood and seed traits respond in parallel to the dominant abiotic gradients: soil types, topographic position, elevation and large‐patch disturbances such as those caused by cyclones–storms, landslips or fires. 4. In contrast, the alpha components of traits were largely uncorrelated among species. Alpha leaf area was not associated with alpha larger seeds, meaning that leaf area and seed size act as independent axes of differentiation among coexisting species. 5. Synthesis . The different correlation structures for alpha and beta components of traits reflect community assembly processes at different scales. Within sites, assembly processes have not created strong linkages among these traits. But across different sites in the landscape, abiotic drivers have created strong linkages.
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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 | 0.001 | 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 | 0.001 |
| 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 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".