Quantifying the role of intra‐specific trait variation for allocation and organ‐level traits in tropical seedling communities
Bibliographic record
Abstract
Abstract Questions Community structure is the outcome of individual‐level interactions. Recent work has shown that disaggregating trait information from the species to the individual level can elucidate ecological processes. We aim to integrate trait dispersion analyses across different aggregation levels including a broad range of traits that allow assessment of patterns of variation among co‐occurring and non‐co‐occurring individuals. We ask the following questions: (1) what is the role of intra‐ and inter‐specific dissimilarity within neighbourhoods vs. across neighbourhoods in promoting trait dispersion; (2) how is trait variation partitioned across all individuals in each study system; and (3) are the results consistent across traits and forests? Location Puerto Rico and China. Methods We measured allocation and organ‐level (e.g. specific leaf area) traits on every individual in two seedling censuses in two tropical rain forests. Then, we partitioned trait variation within and across species, considering its impact on patterns of trait dispersion, and quantifying how these outcomes vary depending on whether allocation‐related or organ‐level traits are considered. Results We found an increase in trait dispersion when individual‐level traits are considered, reflecting conspecific differentiation for allocation of traits. Organ‐level traits, however, do not necessarily promote strong phenotypic displacement within conspecifics. Consistent with this, we found that the majority of variation in allocation of traits was between conspecifics, while most of the variation in organ‐level traits was found between species. Conclusions Overall, trait displacement occurs within and across neighbourhoods, reflecting differentiation at inter‐ and intra‐specific levels. Also, we identify two major phenotypic groups of variation, allocation and organ‐level traits, that constitute two contrasting strategies for response to biotic and abiotic contexts: one highlights ecological differences among individuals, while the other highlights differences among species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".