Past tree influence and prescribed fire mediate biotic interactions and community reassembly in a grassland‐restoration experiment
Bibliographic record
Abstract
Summary Woody plant encroachment of grasslands is occurring globally, with profound ecological consequences. Attempts to restore herbaceous dominance may fail if the woody state is resilient or if intervention leads to an alternate, undesirable state. Restoration outcomes often hinge on biotic interactions – particularly on priority effects that inhibit or promote community reassembly. Following experimental tree removal from conifer‐invaded grasslands, we documented substantial variation in community reassembly associated with the changing abundance of the native clonal sedge Carex inops L.H. Bailey subsp. inops . We explored possible mechanisms for this variation, focusing on the nature and timing of interactions between the meadow community and Carex and on how past tree influence and prescribed fire mediate the outcomes of these interactions. Meadow species increased after tree removal, but less so in burned than in unburned plots. Carex expanded dramatically after fire, particularly where past tree influence had been greater. Meadow species and Carex developed an increasingly negative association over time; pre‐emption was reciprocal, but offset in time and space. Meadow species inhibited Carex through vegetative recovery in areas of limited or recent tree influence, irrespective of fire. Carex inhibited meadow reassembly in areas of greater tree influence, but only with burning. Synthesis and applications . Tree removal and fire imposed across a range of altered meadow states yielded varying outcomes, reflecting biotic interactions and species' regenerative traits that inhibited or promoted reassembly. Fire tended to destabilize the remnant meadow community and, in areas more degraded by encroachment, stimulated release of Carex , which inhibited reassembly. Knowledge of the context dependence of biotic interactions can enhance the effectiveness of restoration by establishing the bounds within which treatments produce desirable or undesirable outcomes.
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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 | 0.001 | 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) | 0.000 | 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".