Fruit Availability Influences Forest Elephant Habitat Use in a Human Dominated Landscape, Campo-Ma’an, Southern Cameroon
Bibliographic record
Abstract
Background and Research Aim African forest elephants ( Loxodonta cyclotis) are critically endangered yet research on factors influencing their resource use is limited in Central Africa. We assessed the influence of fruit availability, land use types, and anthropogenic activity on forest elephant presence and relative abundance in the southwest part of the Campo-Ma’an Technical Operational Unit (CMTOU) to better understand elephant habitat use in human dominated systems and inform elephant management strategies. Methods We used 17 camera trap stations and surveyed 17 line transects to monitor forest elephant presence and relative abundance as a function of fruit availability, tree species richness, and land use types. Our study area spanned a gradient of human disturbance and included a National Park (NP), Forest Management Unit (FMU), and Community Land (CL). Results Forest elephants were more likely to occur in areas with increased fruit availability and tree species richness. Also, the likelihood of their presence was higher in CL than in FMU and NP. Elephant relative abundance was negatively affected by human activities such as hunting and logging. The relationship between elephant relative abundance and fruit availability was stronger in CL and NP as compared to the FMU. Elephant relative abundance was higher during the rainy season. Conclusion Forest elephant habitat use was positively affected by fruit availability across land use types, and negatively affected by human activities in the southwest part of the CMTOU. Implications for Conservation Continued monitoring of elephant responses to food availability in CMTOU is warranted to track changes in elephant habitat use. Knowledge of the distribution of fruiting trees consumed by forest elephants may allow managers to predict hotspots of habitat use, and to therefore develop effective management strategies.
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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 | 0.001 |
| 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) | 0.002 | 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".