Natural and anthropogenic influences on elephants and other ungulates in the Congo forest
Bibliographic record
Abstract
In Central Africa, wildlife populations are increasingly influenced by humans, even in protected areas. This raises the question how spatial patterns of wildlife abundance are affected by human activities and habitat and how these patterns change over time. I address these questions by developing spatial models combined with line transect survey data in two forest sites in Central Africa. In the Odzala National Park in the Republic of Congo, I examine elephant dung abundance data in relation to human threats and protection. In the Okapi Faunal Reserve in the Democratic Republic of Congo (DRC), I developed spatio-temporal models for elephants and other forest ungulates to examine temporal changes in their densities as a result of changes in human impact in the context of a civil war that took place in the region between the two surveys. Covariates related to human influences dominated the observed patterns at both sites. In Odzala, elephant dung densities were mainly influenced by protection. They were higher inside the protected area and closer to anti-poaching patrol routes. In the Okapi Faunal Reserve, populations of all ungulate species declined severely between the two survey periods. Declines in elephant abundance were relatively higher closer to the park boundary and areas of intense human activity. After the war, elephant densities were higher in a small area in the centre of the park that may have acted as a refuge. Forest duikers also declined, but the spatial pattern of their decline was different than that of elephants. Densities dropped more in the southern part of the reserve, probably due to pre-exisisting higher levels of hunting there. Besides explaining spatial patterns of abundance, spatial modeling was shown to be useful in improving the precision of density estimates and in predicting densities across a surface in the Odzala National Park. In summary, humans overwhelmingly determined the distribution and abundance of ungulates in both sites. The civil war in DRC led to a dramatic increase in elephant poaching for ivory which caused a major decline in elephant populations. It aggravated the bushmeat hunting of duikers whose populations also declined sharply.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".