Time since fire shapes plant immaturity risk across fire severity classes
Bibliographic record
Abstract
Abstract Background When fire intervals are shorter than the time required for plants to reproduce, plant populations are threatened by “immaturity risk.” Therefore, understanding how the time between fires influences plants can inform ecosystem management. Quantifying periods of immaturity risk requires investigating the influence of fire intervals across plant life stages, but most studies are indiscriminate of maturity. As fire regimes are multidimensional, it is also important to consider other characteristics of fires such as severity. We conducted a field study in heathy woodland that investigated how fire severity and fire interval influence immaturity risk to serotinous resprouter species, by examining if fire severity interacts with the time since the fire to influence the occurrence of mature individuals and relative abundance of three species: silver banksia (Banksia marginata Cav.), prickly teatree (Leptospermum continentale Joy Thomps), and heath teatree (Leptospermum myrsinoides Schitdl). Results Regression modeling revealed a strong, positive influence of time since the last fire on the proportion of quadrats at a site with mature plants, for all three species. We only detected a small and uncertain influence of fire severity on the proportion of quadrats with mature heath and prickly teatree, and did not observe an effect of fire severity on the maturity of silver banksia. Interestingly, no relationships were observed between time since fire and the relative abundance of plants. That is, only when plant life stages were considered did we detect an effect of fire on plants. Populations of the three species were mostly immature in the first 7 years post-fire, suggesting if sites were uniformly burnt in this time frame, there could be increased risk of local extinctions. Conclusions Our study highlights the importance of examining population processes, such as reproduction, in addition to plant relative abundance. Surprisingly, we did not detect strong differences in plant maturation across fire severity classes; low occurrence of mature plants in recently burnt areas indicated that immaturity risk was high, regardless of fire severity. Ecological studies that distinguish between plant life stages will help to predict the impacts of fire on populations and enhance decision-making. We recommend fire intervals of ≥ 8 years to protect serotinous resprouter plants in heathy woodland vegetation of southern Australia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".