Montane Grassland Resources Drive Gorilla (Gorilla Gorilla) Nesting Behaviours in the Ebo Forest, Littoral Region, Cameroon
Bibliographic record
Abstract
Abstract Great apes show strong attachment to their nesting sites which provides them with substantial survival elements. Their nesting behaviors are influenced by geographical and ecological variables including habitat type, slope, elevation gradients, and sometimes anthropogenic pressures. This study aimed to assess environmental variables that influenced the Ebo gorilla (Gorilla gorilla) nesting behavior in relation to nesting site selection, nest types, and nesting materials. We collected data from January 2013 to November 2017 along reconnaissance tracks (recce, hereafter) using the marked nest counting method. We recorded 0.16 nesting sites per km as an encounter rate, with an average number of four nests per gorilla group. The mean nest diameter was 90.33 ± 23.92 cm (n = 640, range 25–199 cm). Ebo gorillas preferred nesting sites at high altitude located in the grassland areas with open canopy, ligneous undergrowth composition and very closed visibility. Ebo gorillas used more than 281 plant species as materials for nesting with Marantaceae and Zingiberaceae species being the most common material used. Terrestrial herbaceous nests were the most common nest type (55%). During the dry season, gorillas visited more often the mature forest habitat and mostly constructed arboreal nests. Finally, reuse of nesting sites by Ebo gorillas was minimal (16%), and visitation period occurred from 3 days to 33 months. Our study provides the first systematic investigation of gorilla nesting behavior within the Ebo forest constituting therefore an essential starting point for the long-term conservation planning of this little-known population.
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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 | 0.001 |
| 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".