Trait-Based Community Assembly along an Elevational Gradient in Subalpine Forests: Quantifying the Roles of Environmental Factors in Inter- and Intraspecific Variability
Bibliographic record
Abstract
Understanding how communities respond to environmental variation is a central goal in ecology. Plant communities respond to environmental gradients via intraspecific and/or interspecific variation in plant functional traits. However, the relative contribution of these two responses to environmental factors remains poorly tested. We measured six functional traits (height, leaf thickness, specific leaf area (SLA), leaf carbon concentration (LCC), leaf nitrogen concentration (LNC) and leaf phosphorus concentration (LPC)) for 55 tree species occurring at five elevations across a 1200 m elevational gradient of subalpine forests in Yulong Mountain, Southwest China. We examined the relative contribution of interspecific and intraspecific traits variability based on community weighted mean trait values and functional diversity, and tested how different components of trait variation respond to different environmental axes (climate and soil variables). Species turnover explained the largest amount of variation in leaf morphological traits (leaf thickness and SLA) across the elevational gradient. However, intraspecific variability explained a large amount of variation (49.3%-76.3%) in three other traits (height, LNC and LPC) despite high levels of species turnover. The detection of limiting similarity in community assembly was improved when accounting for both intraspecific and interspecific variability. Different components of trait variation respond to different environmental axes, especially soil water content and climatic variables. Our results indicate that intraspecific variation is critical for understanding community assembly and evaluating community response to environmental change.
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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 | 0.001 |
| 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) | 0.000 | 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".