Spatio‐temporal changes in the understory heterogeneity, diversity, and composition after fires of different severities in a semiarid oak (<scp><i>Quercus brantii</i></scp> Lindl.) forest
Bibliographic record
Abstract
Abstract In arid and semiarid forests and rangelands, native ranchers and farmers frequently use fire as a tool to improve soil fertility and vegetation composition, and to facilitate soil tilling. Investigating changes in ecosystem characteristics after these measures is of great importance for establishing management and recovery strategies. This study aimed to investigate spatial–temporal changes in the understory heterogeneity, diversity and composition after fires of different severities in a Brant's oak (Quercus brantii Lindl.) forest. Vegetation sampling was monitored in 14 patches including unburned sites (UBN), burned sites with low fire severity after 1, 5, and 10 years (LFSO, LFSF, and LFST, respectively), and burned sites with high fire severity after 1, 5, and 10 years (HFSO, HFSF, and HFST, respectively). Fire severity and time since fire significantly affected diversity indices with the lowest values of richness, evenness and diversity in high‐severity fires, while the highest values were observed in low‐severity fires. Time since fire did not significantly affect the understory evenness in both fire severities. However, species diversity and richness in low‐severity fires decreased with time since fire while the reverse was observed in high‐severity fires. The results of a detrended correspondence analysis indicated that the severity and time since fire significantly changed vegetation composition. The largest changes in vegetation composition compared to control sites were observed in HFSO, and then in HFSF and HFST. Both fire severity and time since fire caused changes in the heterogeneity of plant communities. We concluded that the use of low‐severity fires can be suitable for maintaining and increasing the heterogeneity of understory vegetation in semiarid forest ecosystems. However, low‐severity fires have also slightly, while not significantly, increased evenness and can therefore potentially reduce the ecosystem functions of dominant species in such semiarid regions. This could mitigate the positive effect of fire on vegetation heterogeneity and necessitate further investigations.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".