Which plant traits determine abundance under long‐term shifts in soil resource availability and grazing intensity?
Bibliographic record
Abstract
Summary 1. Soil resource availability and disturbance are widely recognized as key drivers of plant community structure. However, the relative importance of different plant traits in determining species abundance following shifts in soil resource availability and disturbance remains little studied, particularly in long‐term experiments. 2. We studied trait‐based plant community assembly in a 27‐year grassland experiment where 25 plant species were sown into resident vegetation, after which annual manipulations of soil resource availability (five levels of superphosphate fertilizer; the highest level was also irrigated) and disturbance (three ‘mob‐grazed’ sheep grazing intensity levels: lax, moderate, hard) were applied. We used community assembly through trait selection (CATS) models based on entropy maximization to predict species relative abundances and to quantify the relative importance of each trait in determining abundance. 3. Plant species were primarily differentiated along a trade‐off axis corresponding to traits promoting rapid growth (e.g. high leaf [N] and specific leaf area [SLA]) vs. those promoting long leaf life span. Using 12 traits, the CATS model predicted >80% of the variation in the relative abundances of 51 species, suggesting that trait‐based filtering was important. 4. Species with leaf attributes that reduce nutrient losses held a long‐term advantage under the lowest soil resource availability, whereas those associated with a rapid growth rate became dominant under soil resource addition. Species with thinner leaves were also favoured under greater soil resource availability, which may reflect a strategy to maximize SLA without sacrificing leaf density and thus maintain leaf structural defences under grazing disturbance. Greater leaf [S] and the ability to symbiotically fix atmospheric N were favoured under greater soil resource availability. Greater plant height, thinner leaves and higher leaf [N] were favoured under lower grazing intensity. 5. Synthesis. Our results highlight the importance of species functional differences to understand how plant communities react to increases in soil resource availability and disturbance, two important and inseparable components of land‐use change in grasslands world‐wide.
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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".