Vigilance Behaviour of Wild Herbivores when Foraging With or Without Livestock
Bibliographic record
Abstract
In African savannas, and many other rangelands around the world, wildlife presently find themselves interacting with livestock. Many studies have been conducted on vigilance behaviour in response to presence of predators on foraging grounds, but few scientists have included the presence of livestock and how this affects vigilance when foraging together with wild herbivores. As Ngorongoro Conservation Area (NCA) is an important example of wildlife grazing together with livestock, this phenomenon must be understood to achieve a sustainable land use management plan, particularly in Ngorongoro Conservation Area and in other protected areas. Behavioral observations of wildlife and livestock species were conducted from a vehicle driving along transects within NCA. Once a group was sighted the vehicle was stopped and sighting information recorded. Four species of wild herbivores including plains zebra (Equus burchelli), Thomson’s gazelle (Gazella thomsonii), Grant’s gazelle (Gazella granti) and Wildebeest (Connochaetes taurinus) were studied together with the following livestock species including cattle (Bos taurus), goats (Capra aegagrus hircus) and sheep (Ovis aries), in different seasons. 158 groups were recorded. In dry season 47 non –mixed groups (without livestock) and 30 mixed groups (with livestock) were recorded, while, during wet season 49 non -mixed groups and 32 mixed groups were recorded. Behavior was classified as feeding, grooming, laying down, ruminating, grooming, moving, and looking with head up (vigilance) with the use of the focal animal sampling method. Wildlife decreased their foraging time while they increased vigilance behavior when livestock were present; however, more studies are still for a wider conclusion.
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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".