Resource selection, utilization and seasons influence spatial distribution of ungulates in the western Serengeti National Park
Bibliographic record
Abstract
Abstract Understanding herbivore selection and utilization of vegetation types is fundamental to conservation of multispecies communities. We tested three hypotheses for how ungulate species select their habitats and how this changes with season: first, resources are distributed as a mosaic of patches so that ungulates are also distributed patchily; this distribution reflects habitat selection, which changes with season, the different ungulates behaving differently. Second, resources become scarcer in the dry season relative to those in the wet season. If interspecific competition prevails, then all species should show a contraction of habitats chosen. Third, if predation is limiting, competition will be minimal, and hence, habitat selection by herbivores will not differ between seasons. We used frequencies of occurrence in four common vegetation types in western Serengeti National Park to determine selection coefficients and utilization patterns and Chi‐square analysis to test the hypotheses. The results showed that selection changes differently in each species, agreeing with the first hypothesis. Herbivores did not all become more selective, as predicted by the competition hypothesis, nor did selection remain the same across seasons, as predicted by the predation hypothesis. These results can be useful in constructing habitat suitability maps for ungulate species with special conservation needs.
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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 | 0.001 | 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".