Assessing trait driver theory along abiotic gradients in tropical plant communities
Bibliographic record
Abstract
ABSTRACT Despite the many studies using trait-based approaches to assess the impact of environmental gradients in forest trait composition, the relative roles of (i) intraspecific variation in community assembly and (ii) microclimatic or fine scale abiotic variation in shaping local trait diversity remain poorly understood. To advance their understanding we tested several assumptions and predictions of trait driver theory (TDT). We quantified the shape of trait distributions related to tree carbon, nutrient economics and stem hydraulics across a small-scale but steep gradient of soil water availability. We utilized a unique and steep environmental gradient in the coastal Brazilian Atlantic forest ( restinga ) communities that spans a very short distance (207 ±60 meters). We collected leaf and wood samples of tree species across 42 patches (or plots) of restinga forest. Furthermore, to detect if species directionally shift in niche space, we analyzed species composition in multidimensional hypervolume space. Despite short geographic distances, we observed large shifts in species replacement and intraspecific variation reflected by a directional shift in plant function. Consistent with TDT, we observe (i) trait distributions that are skewed in directions consistent with a forest responding to recent hotter and drier; (ii) peaked trait distributions, indicating strong functional convergence; and (iii) conditions decreasing means and variances of several leaf carbon and nutrient economic traits as well as stem hydraulic traits. Synthesis. Observed species replacements along the water table gradient and interspecific measures of functional diversity (community kurtosis and skewness) are consistent with strong phenotype/environmental matching of plant carbon, nutrient, and hydraulic strategies. We observe environmental filtering in both extremities of the gradient, selecting for acquisitive (wet) to conservative (dry) setup of traits. Similarly, species that span the entire water availability gradient are characterized by directional intraspecific shifts in multi-trait space that mirror interspecific shifts. Strong environmental gradients across short spatial scales provide unique systems to accurately assess assembly processes and address long-held assumptions and timely hypothesis predicted by trait driver theory.
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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 | 0.001 |
| 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) | 0.001 | 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".