Management legacies shape decadal‐scale responses of plant diversity to experimental disturbance regimes in fragmented grassy woodlands
Bibliographic record
Abstract
Summary Understanding multiple ecological determinants of plant diversity and composition underpins good vegetation management. In mesic ecosystems, ecological theory and empirical data predict that moderate to high disturbance promotes native plant diversity, but relationships between disturbance and other drivers of diversity are poorly understood. We examined local determinants of native plant diversity and composition through 12‐year fire, mowing and grazing experiments in two mesic grassy woodland remnants in fragmented agricultural landscapes of south‐eastern Australia. Remnants were representative of diverse woodlands , but had contrasting management histories. We hypothesized that (1) disturbance is a dominant driver of plant diversity and composition and (2) moderate to high disturbance promotes diversity independent of other drivers or disturbance agent. Contrary to our first hypothesis, rainfall explained an overwhelming 31–60% of variation in native plant richness and native forb cover (hereafter diversity), while aspects of disturbance regime accounted for a significant but moderate 3–8%. The magnitude of disturbance effects was often rainfall dependent, and similar to that of other local determinants such as spatial heterogeneity. Disturbance also influenced native forb composition at one site. The direction of disturbance effects on diversity did not depend on disturbance agent, but differed markedly between sites in accordance with their management history. At the cleared site with a history of frequent burning, diversity declined significantly with time since burning or mowing. By contrast, at the long‐unburnt, wooded site, diversity declined with frequent fire and with lagomorph and macropod grazing. These contrasting responses indicate that moderate to high disturbance is not consistently beneficial for diversity in mesic grassy ecosystems, contradicting our second hypothesis. Instead, disturbance responses are dependent on interactions with other drivers, potentially including assemblage filtering associated with historical fire regime and increased understorey productivity associated with tree clearing. Synthesis and applications : The magnitude and direction of disturbance effects on diversity depend on interactions with other drivers, hence a single prescription for disturbance regime across different remnants of fragmented grassy ecosystems is not appropriate. Decadal‐scale responses of diversity to disturbance may be shaped by historical disturbances, suggesting instead that management be guided by historical regime.
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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 | 0.001 |
| 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".