Harsh environmental regimes increase the functional significance of intraspecific variation in plant communities
Bibliographic record
Abstract
Abstract The relative importance of intraspecific trait variation ( r ITV) for functional diversity (FD) in plant communities is increasingly apparent, but the influence of abiotic factors on the balance between intraspecific and interspecific effects in contrasting environments is uncertain. Drawing on a novel conceptual and analytic framework, we quantified r ITV as the ratio of FD associated with between‐population ITV ( Intra _FD) and the interspecific variation ( Inter _FD) due to species turnover across sites. We predicted that r ITV would increase with environmental harshness as a result of decreasing interspecific variation and concomitant increases in between‐population ITV. We empirically tested this prediction in a comparison of r ITV for five traits (mature plant height, SLA, leaf dry matter content and the foliar concentrations of nitrogen and phosphorus (P)) characterizing species in Tibetan alpine meadow communities from contrasting environmental regimes. We found that across sites: (a) r ITV for all five traits increased with environmental harshness not only through decreasing Inter _FD but also through increasing Intra _FD; (b) increases in r ITV were mostly attributable to declines in soil resources, especially soil P, with lower soil P significantly associated with lower Inter _FD but higher Intra _FD for most traits; and (c) although neither Intra _FD nor Inter _FD was significantly impacted by species richness for any traits, a higher r ITV for SLA was significantly associated with low species richness. Taken together, these results support our hypothesis that the functional significance of ITV in plant communities increases with environmental harshness. Moreover, in the Tibetan alpine meadows we studied, the opposing intraspecific and interspecific responses along a gradient of P availability appear to act as a buffering mechanism stabilizing the functional structure of communities on P deficient soils. Our novel conceptual and analytical framework provides a template for evaluating the generality of this favourable mismatch between intraspecific and interspecific responses in FD along environmental gradients. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.000 | 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".