Grass greenness and grass height promote the resource partitioning among reintroduced Burchell's zebra and blue wildebeest in southern Mozambique
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Bibliographic record
Abstract
Abstract Differences in the selection of habitat and specific dietary items support resource partitioning and coexistence of sympatric African grazing herbivores, such as zebra and wildebeest. In Maputo Special Reserve ( MSR ), southern Mozambique, these two species were extirpated during the civil war (1977–1992); since 2010, they have been reintroduced into the Reserve. Identifying the resource selection by reintroduced species and how these species coexist, while utilising the same resources, is both of ecological interest and important for the management of wildlife communities and parks. This is a key application of our research. Therefore, the present study investigated resource partitioning between Burchell's zebra ( Equus burchelli , Smuts 1832) and blue wildebeest ( Connochaetes taurinus , Burchell 1823) in the MSR . We conducted the study from July 2016 to June 2017. The data were collected by direct observation, driving the vehicle along the reserve's roads that covered the vegetation communities where zebras and wildebeest are known to commonly occur. The composition of the diet and specific features of the grass grazed by the two species, including greenness, height, and the number of stems, were assessed. The widely available grass, Aristida barbicollis , contributed most to the diet of both herbivores. The dietary overlap between the two herbivores was higher during the dry season (95%) than wet season (86%). Resources partitioning appears to be determined, principally, by the height and greenness of the grass, with the zebra grazing taller grass, which may facilitate the access of the wildebeest to the greener, lower proportion of the forage. That results follow the expectation that, among native herbivores, overlap in resource use is not expected based on evolutionary segregation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it