The influence of food availability, quality and body size on patch selection of coexisting grazer ungulates in western Serengeti National Park
Bibliographic record
Abstract
Context Differences in body size and mouth morphologies influence dietary resource separation among savanna ungulates, and this influences their distribution across landscape. Aim The aim was to understand the influence of body size and mouth morphology differences on both diet and patch selection by ungulate species in western Serengeti. Two hypotheses were tested: (1) for ungulate species, the relative body sizes and muzzle widths (mean muzzle width range: 3.1- 9.85) relate directly to food biomass, and to quality of diets selected in the wet season when food is abundant; (2) in the wet season, if food is not limiting, similar-sized species should exhibit greater dietary niche overlap than ungulate species that differ greatly in body size and muzzle width; moreover, similar-sized species exhibit less dietary niche overlap than ungulate species that differ greatly in body size. Methods In the western Serengeti ecosystem, road transects and direct observation were used to obtain data on the distribution and diet of five ungulate species namely buffalo, zebra, wildebeest, topi and impala; which have of varying sizes (range: 70–630 kg) feeding in three different vegetation types. Grassland biomass, structure, nutrient content and ungulate use were measured at sites along transects. Key Results Results indicated that large-bodied ungulates utilised patches of greater food abundance compared with those of smaller ungulates. Body mass was also negatively correlated with diet quality, so that smaller animals ate higher protein and lower-fibre foods, as predicted. Diet niche overlap (niche similarity) showed a strong positive relationship with body mass differences among ungulate species, in support of the second of the two predictions from this hypothesis, namely that dissimilarly sized species could eat the same food. Conclusion Overall, the results suggest that in this savanna system, variation in ungulate body size influences resource separation even in the food-abundant wet season, and that this helps multiple species to co-exist. Implications Implementing more focused conservation strategies will improve wildlife habitat quality by integrating fire as a forage management tool with grazing preferences to promote forage heterogeneity in protected areas.
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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 | 0.000 |
| 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".