Chronic fertilization and irrigation gradually and increasingly restructure grassland communities
Bibliographic record
Abstract
Abstract Scientists have known for over a century that resource addition can lead to species loss from plant communities. Recent studies have also shown that resource addition can substantially restructure communities by altering their functional and taxonomic composition—even when species richness remains unchanged. Understanding which aspects of community structure are impacted by different resources and over which timescales will provide insight for management decisions and may also elucidate which measures can act as early warning indicators for subsequent changes in the community. Here, we take advantage of a long‐term factorial experiment to understand how grassland plant communities respond to a decade of nitrogen fertilization (14 g N·m −2 ·yr −1 ) and irrigation (25 mm water/week during the growing season). After 10 yr, fertilization and irrigation decreased species richness by 22% and 9%, while functional richness decreased by 31% and 41%. Abundance‐weighted functional distance between treatments and controls increased by 55% and 24%, respectively. We expected that abundance‐weighted measures would shift before presence–absence‐based measures, but found limited evidence for this. Instead, our results suggest that species gains, which can occur quickly because they require the addition of only one individual, may serve as early indicators for subsequent community restructuring in the opposite direction. Overall, both chronic nitrogen fertilization and irrigation tended to have gradual and increasing impacts on community structure, but the magnitude of these effects varied greatly depending on the aspect of community structure investigated. Further study will be needed to determine the extent to which our results can be generalized to other resources or sites in order to develop management strategies to maintain both taxonomic and functional trait diversity in the face of chronic resource changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".