Fire return intervals and recruitment affect population growth rate of canopy trees in tall open forest in humid savanna
Bibliographic record
Abstract
Abstract Savannas are the major biome in tropical regions of the globe, defined as sparsely wooded regions with a continuous herbaceous layer of mainly C 4 grasses where rainfall is distinctly seasonal. Fire is a common feature of most savannas. The largest protected areas of savannas are found in sparsely populated monsoonal northcentral Australia with strong annual wet and dry seasons. The most common vegetation type is relatively intact, tall (<15 m), open forests where Eucalyptus canopy trees form the basic structure. Over the past half century, traditional indigenous fire regimes were largely replaced by contemporary fires where individual trees may experience fire as often as 3 out of 5 years. The potential for long‐term persistence of the canopy tree populations is an open question. A stage‐based population model of the canopy trees was previously developed to address this question, drawing on data from three decades of experimental field studies wherein the survival, growth, and reproduction of individual marked trees were recorded under different seasonal fires and understory types to produce transition matrices among eight life history stages, and used to calculate population growth rates (λ). Here, we apply that model to determine how λ varies across a range of fire return intervals from 1 to 12 years for both early and late dry season fires, in two different understory types. We also explore the sensitivity of λ to two key life history parameters: recruitment and seedling survival. Minimum fire return intervals of 2–5 years were generally required for λ ≥1 that would allow populations to persist; these were shorter with stochastic year‐to‐year timing of fires and with higher recruitment rates. Uniquely, under certain conditions, there was also a maximum fire return interval above which λ <1, creating a “window” of fire return intervals that allowed canopy tree populations to persist. Mechanisms underpinning results as well as implications for savanna structure, alternate states, cyclical dynamics, future research, and management by fire are discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".