Impacts of forest fire frequency on structure and composition of tropical moist deciduous forest communities of Bandhavgarh Tiger Reserve, Central India
Bibliographic record
Abstract
Forest fire is one of the prominent factors especially in tropical seasonal forests that have a variety of consequences on ecosystem composition, structure and function depending upon the type of fire, fire intensity, and fire frequency. Forest fire incidences have been increasing in the last few decades especially in tropical moist forests, raising the concerns for forest restoration and management since the inhabiting plants in these forest communities largely lack adaptive strategies. The present study was carried out in tropical moist deciduous forests to (A) understand the patterns of forest fire frequency by delineating the fire affected areas and (B) evaluate the impacts of forest fire frequency on species composition, diversity and regeneration of a moist deciduous forest of Central India. A fire frequency map was prepared using Landsat satellite images from 2005 to 2021 for the study purpose. Vegetation data were collected from field surveys for each fire frequency class separately. Results of the present study showed that 25.12 % area was affected by low frequent fires, 2.92 % by moderate frequent fires and 0.099 % by high frequent fires, whereas 71.85 % area remained unaffected by fire. The diversity of the tree layer was highest in the low fire frequency class whereas, for the sapling layer, it was highest in unburned areas. Overall, the negative impact of fire frequency on species diversity was observed for the tree and sapling layers. Favorable effects of fire on young current year recruitments were observed as a result of the removal of seed dormancy. Results indicate that moderate fire frequency in moist deciduous forests helps to increase tree density. Contrary to this, unburned areas are suitable for species diversity of seedlings and saplings which consequently decides the composition of mature vegetation. Overall, a negative impact of fire frequency on density was observed for the sapling and seedling layers. Our study concludes that higher fire frequency is detrimental to both the density and diversity of tree, sapling and seedling layers, particularly in tropical moist deciduous forest communities of Central India. It is expected that fire incidences are likely to increase with the increasing temperatures as a result of climate change. In this context, the present study would be highly valuable for forest policy development as information on the impact of fire in tropical moist forests is lacking especially from Central Indian region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".