Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in <scp>a M</scp>editerranean rangeland
Bibliographic record
Abstract
Summary Understanding how environmental factors drive plant community assembly remains a major challenge in community ecology. The strength of different assembly processes along environmental gradients, such as environmental filtering and functional niche differentiation, can be quantified by analysing trait distributions in communities. While environmental filtering affects species occurrence among communities, functional divergence or convergence is strongly related to species abundances within communities, which few studies have taken into account. We examine the trait‐mediated effect of these two processes along a stress‐resource gradient. We measured species abundances and the distributions of eight traits related to vegetative and regenerative phases in plant communities along a gradient of soil depth and resource availability in Mediterranean rangelands. We quantified environmental filtering, defined as a local restriction of trait range, and trait divergence, based on abundance‐weighted trait variance, using a two‐step approach with specifically designed null models. Communities presented a clear functional response to the soil gradient, as evidenced by strong trends in community‐weighted trait means. We detected environmental filtering of different traits at both ends of the gradient, suggesting that, contrary to widespread expectations, trait filtering may not necessarily be the result of abiotic filtering under harsh conditions but could likely also result from biotic interactions in productive habitats. We found marked shifts in trait abundance distributions within communities along the gradient. Vegetative traits (e.g. leaf dry matter content) diverged on shallow soils, reflecting the coexistence of distinct water‐ and nutrient‐use strategies in these constrained habitats and converged with increasing soil resource availability. By contrast, regenerative traits (e.g. seed mass) tended to diverge towards deeper soils, while plant reproductive heights diverged all along the gradient. Synthesis: Our study highlights how the combination of abundance data with traits capturing different functional niches is critical to the detection of complex functional responses of plant communities to environmental gradients. We demonstrate that patterns of trait divergence and filtering are strongly contingent on both trait and environment such that there can be no expectation of a simple trend of increasing or decreasing functional divergence along a gradient of resource availability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".