Changes in Elephant Abundance Affect Forest Composition or Regeneration?
Bibliographic record
Abstract
Abstract While overall numbers of African elephant have declined dramatically in recent times, some populations are now confined to protected areas and are locally overabundant—an undesirable situation for both biodiversity conservation and elephants. In forested protected areas, options to manage elephants are limited because it is difficult to safely approach animals, yet it is vital that these populations are managed because browsing by elephants can dramatically alter forest ecosystems. Using data collected over 50 yr in Kibale National Park, Uganda, we examine the prediction that increasing elephant numbers and associated changes in their foraging behavior have caused a shift in tree community composition. Although the relative abundance of elephants increased significantly between 1996 and 2010, the population structure of their preferred tree food species did not change, nor did tree community composition change in favor of species able to re‐sprout after elephant damage. Furthermore, over the last 50 yr Kibale elephants have not become more selective foragers, as would be expected if more nutritious tree species were declining. However, elephants are more abundant in disturbed areas dominated by shrubs and grasses and appear to have arrested forest succession in these areas. At their current abundance, elephants have not selectively altered the composition of intact old growth forest, but they do inhibit the regeneration of disturbed areas.
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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 | 0.000 | 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 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".