Can functional traits predict plant community response to global change?
Bibliographic record
Abstract
Abstract One primary goal at the intersection of community ecology and global change biology is to identify functional traits that are useful for predicting plant community response to global change. We used observations of community composition from a long‐term field experiment in two adjacent plant communities (grassland and coastal sage shrub) to investigate how nine key plant functional traits were related to altered water and nitrogen availability following fire. We asked whether the functional responses of species found in more than one community type were context dependent and whether community‐weighted mean and functional diversity were significantly altered by water and nitrogen input. Our results suggest varying degrees of context dependency. We found that plants with high leaf nitrogen concentration (specifically nitrogen fixers), shallow roots, and low leaf mass per unit area and plant‐level transpiration were similarly negatively influenced by added nitrogen across community types. In contrast, responses to water manipulations exhibited greater context dependency; plants with high water‐use efficiency, lower plant‐level transpiration rates, and shallower roots were negatively impacted by simulated drought in the shrub‐dominated community, but there was no significant relationship between these traits and changes in water inputs in the grassland community. Similarly, we found context dependency in community‐wide trait responses to global change. Functional diversity tended to be higher in plots with reduced water as compared to those with added water in grassland, while the opposite trend was observed in coastal sage scrub. Our results indicate that some traits are strong predictors of species and community response to altered water and nitrogen availability, but the magnitude and direction of the response may be modulated by the abiotic and biotic context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".