Late Pleistocene montane forest fire return interval estimates from Mount Kenya
Bibliographic record
Abstract
Abstract Past forest fire events and fire frequencies are reconstructed with sediment–charcoal records at lake catchment spatial scales. Few quantitative palaeofire analyses exist in tropical montane forests, where fire return intervals are long (decadal and centennial scales) because of the infrequency of fire weather and fuel conditions. Fire return intervals are a key characteristic of fire regimes and changing fire frequencies rapidly alter land cover compositions and vegetation structure. Charcoal records from small lakes with relatively small catchments covered with dense forest provide an opportunity to reconstruct low‐frequency, high‐severity fires through a time series decomposition approach to identify charcoal peaks above a varying background rate as a proxy for palaeofire events. The sediment core from Rumuiku wetland on Mount Kenya, equatorial eastern Africa, accumulated a nearly linear age–depth model and provided a high temporal resolution (10 years cm –1 ) sieved charcoal count record (>125 µm). Pollen analysis showed a significant change in montane forest assemblage occurred at 21 200 cal a bp from a montane forest with abundant Podocarpus and Juniperus to a forest with more abundant Hagenia . This change in forest altered the vegetation composition and structure with concomitant changes to the fire regime. Forest biomass in the Hagenia forests decreased and it is likely that fire activity qualitatively changed toward lower intensity and lower severity fires. The quantitative fire event reconstruction focuses on the interval from 27 000 to 16 500 cal a bp and the older montane forest that experienced higher severity fires from 27 000 to 21 200 cal a bp, which reconstructed a temporally heterogeneous fire regime with fire return intervals that ranged from 30–430 years and a mean of 120 years (median 160 years) in the catchment. These are the first estimates of fire return intervals of mountain forests in eastern Africa. We then explore the potential for further comparative research and incremental research contributions to improve quantitative and qualitative palaeofire research in tropical forest ecosystems. We discuss the potential to use these types of data for characterizing variables of fire regimes prior to ostensibly significant modification by anthropogenic activity as well as during the recent past as human land use pressures increased within Afromontane forests.
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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 | 0.002 | 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 | 0.002 | 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".