Data from: Past tree influence and prescribed fire exert strong controls on reassembly of mountain grasslands after tree removal
Bibliographic record
Abstract
Woody-plant encroachment represents a global threat to grasslands. Although the causes and consequences of this regime shift have received substantial attention, the processes that constrain reassembly of the grassland state remain poorly understood. We experimentally tested two potentially important controls on reassembly—the past influence of trees and the effects of fire—in conifer-invaded grasslands (mountain meadows) of western Oregon. Previously, we had reconstructed the history of tree invasion at fine spatial and temporal resolution. Using small subplots (10 × 10 m) nested within larger (1-ha) experimental plots, we characterized the fine-scale mosaic of encroachment states, ranging from remnant meadow openings (minimally altered by trees) to century-old forests (lacking meadow species). Subsequently, we removed trees from six plots, of which three were broadcast burned and three remained unburned (except for localized burn piles). Within each plot, subplots were sampled before and periodically after tree removal to quantify the individual and interactive effects of past tree influence and fire on grassland community reassembly. Adjacent, uninvaded meadows served as references sites. ‘Past tree influence’ was defined as the multivariate (structural or compositional) distance of subplots to reference meadows prior to tree removal. ‘Reassembly’ was defined as the distance, or change in distance, to reference meadows at final sampling. Consistent with theory, we observed greater reassembly of plant community structure than of composition, as loss of meadow specialists was offset by establishment of disturbance-adapted meadow generalists of similar growth form. Nevertheless, 8 years after tree removal, most subplots remained structurally and compositionally distinct from reference meadows. Furthermore, fire had both destabilizing and inhibitory effects: it reduced survival of meadow specialists across the range of encroachment states and, where past tree influence was greater, it stalled reassembly by promoting expansion of a highly competitive native meadow sedge. The slow pace of reassembly, despite abundant open space, suggests strong seed limitation—a condition exacerbated by burning. We present a novel test of the importance of past tree influence and fire for restoration of tree-invaded grasslands, offering insights into how constraints on community reassembly vary along a continuum of tree-altered states.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".