Influence of Timber Extraction Routes on Central African Small‐Mammal Communities, Forest Structure, and Tree Diversity
Bibliographic record
Abstract
Despite increasing pressure to harvest timber from African tropical forests, the short- and long-term ecological effects of qualitative and quantitative variation in extraction practices rarely have been examined. At a site in the southwestern Central African Republic, we surveyed rodent and tree communities and vegetation structure in unlogged forest and along skid trails and secondary and primary access roads at 12 and 19 years after logging. The most important source of variation among transects was the type of logging road: primary and secondary access roads showed the greatest change and skid trails the least. An intercorrelated suite of changes occurred along the margins of the roads, including changes in rodent community composition, increases in rodent abundance and diversity, changes in the height distribution of rodent abundance, increases in understory foliage density, and decreases in sapling density and tree species richness. Ecological changes along the secondary roads were nearly as strong as those along primary roads, despite the fact that secondary roads had been abandoned immediately after logging, whereas primary roads had been traveled up to the time of the research. Continuing edge-induced effects along graded road margins at between 12 and 19 years after logging were indicated by differences in tree species composition, sapling and tree densities, and understory density. Our results support conclusions of increased disturbance to rainforest communities with increasingly destructive road construction techniques and suggest that canopy damage rather than stem damage is the most appropriate measure of logging damage. Although minimizing the length of access roads is important in reducing ecological effects, it should not be achieved at the expense of increased canopy damage. Rodent communities appear to be an easily measured indicator of these ecological changes and may be responsive to landscape-level changes in forest cover and degradation.
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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 | 0.001 |
| 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) | 0.001 | 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".