Higher local intra- than interspecific variability in water- and carbon-related leaf traits among Neotropical tree species
Bibliographic record
Abstract
BACKGROUND AND AIMS: Intraspecific variability in leaf water-related traits remains little explored despite its potential importance in the context of increasing drought frequency and severity. Studies comparing intra- and interspecific variability of leaf traits often rely on inappropriate sampling designs that result in non-robust estimates, mainly owing to an excess of the species/individual ratio in community ecology or, on the contrary, to an excess of the individual/species ratio in population ecology. METHODS: We carried out virtual testing of three strategies to compare intra- and interspecific trait variability. Guided by the results of our simulations, we carried out field sampling. We measured nine traits related to leaf water and carbon acquisition in 100 individuals from ten Neotropical tree species. We also assessed trait variation among leaves within individuals and among measurements within leaves to control for sources of intraspecific trait variability. KEY RESULTS: The most robust sampling, based on the same number of species and individuals per species, revealed higher intraspecific variability than previously recognized, higher for carbon-related traits (47-92 and 4-33 % of relative and absolute variation, respectively) than for water-related traits (47-60 and 14-44 % of relative and absolute variation, respectively), which remained non-negligible. Nevertheless, part of the intraspecific trait variability was explained by variation of leaves within individuals (12-100 % of relative variation) or measurement variations within leaf (0-19 % of relative variation) and not only by individual ontogenetic stages and environmental conditions. CONCLUSIONS: We conclude that robust sampling, based on the same number of species and individuals per species, is needed to explore global or local variation in leaf water- and carbon-related traits within and among tree species, because our study revealed higher intraspecific variation than previously recognized.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".