Species responses to changing precipitation depend on trait plasticity rather than trait means and intraspecific variation
Bibliographic record
Abstract
Abstract Trait‐based approaches are key to develop mechanistic understanding of differences in plant species performance under environmental change. While mean trait values have been widely used to link functional traits to species performance, the contribution of intraspecific trait variation and trait plasticity remains unclear. Moreover, environmentally induced changes in species biomass are caused by changes in the number of individuals and individual growth rate, both of which should be influenced by trait differences and plasticity. Our goal in this study is to use trait‐based information to explain species performance via changes in species abundance and individual weight. We measured the mean, intraspecific variation and plasticity of nine above‐ground plant traits, and a further three mean root traits from 10 common species in a precipitation manipulation experiment in semi‐arid grassland. We used this trait information to explain differences in the responses of species biomass, abundance and mean individual weight to changing precipitation. Species responses were calculated as the normalised slopes of the regressions between species biomass, abundance and individual weight with the manipulated precipitation amount. We found strong differences in species responses to changing precipitation for species biomass, abundance and mean individual weight. Reduced precipitation decreased biomass, abundance and mean individual weight for some species, but increased them for others. Biomass and mean individual weight of species with resource‐acquisitive traits, such as shallow rooted species, showed stronger positive responses to changing precipitation compared to resource‐conservative traits, like those with deep roots. For above‐ground traits, trait plasticity was the strongest predictor of species responses compared to mean traits and intraspecific trait variation. In addition, trait plasticity regulated changes in species biomass more via changes in species abundance than mean individual weight. These results indicate that trait plasticity is a key driver for determining species‐specific responses to changing precipitation and needs more consideration for understanding and predicting ecosystem structure and functioning in future climate scenarios. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".