Increased rainfall variability and nitrogen deposition accelerate succession along a common sere
Bibliographic record
Abstract
Abstract Ongoing climate change is increasing rainfall variability in many parts of the world; in particular, the heaviest rainfall events are becoming heavier. In terrestrial ecosystems, nitrogen deposition is increasing as a result of emissions from fossil fuel burning and volatilization of nitrogen‐based fertilizers. These changes in the timing and rate of resource inputs can impact plant communities by altering competitive dynamics, succession, and community composition. In many systems, these are occurring alongside successional dynamics, making it difficult to tease apart mechanisms. Here, we resampled a nitrogen by rainfall variability manipulation experiment in a restored tallgrass prairie to examine the relative role of background community dynamics and treatment effects on plant diversity. During the treatment period, nitrogen addition and increased rainfall variability reduced diversity. Here, four and five years after the treatments were halted, we found similarly low levels of diversity across all treatments—an effect driven by dominance of a tall, fast‐growing, clonal forb, Solidago canadensis . The convergence of plots toward a low diversity state suggests that all experimental communities were gradually becoming dominated by S. canadensis , including in the absence of rainfall or nitrogen treatments. In contrast to short‐term findings from the same experiment, we conclude that our treatments accelerated succession toward a tall, clonal forb‐dominated community along an existing sere, but did not fundamentally alter longer‐term community composition—a result that was only apparent several years after the conclusion of the experiment. These findings reinforce the need to interpret the results from short‐term experimental manipulations within the context of long‐term successional change.
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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) | 0.003 | 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".