Serengeti real estate: density vs. fitness‐based indicators of lion habitat quality
Bibliographic record
Abstract
Habitat quality is typically inferred by assuming a direct relationship between consumer density and resource abundance, although it has been suggested that consumer fitness may be a more accurate measure of habitat quality. We examined density vs. fitness-based measures of habitat quality for lions in the Serengeti National Park, Tanzania. A 40-year average of female reproductive success (yearling cubs per female) was best explained by proximity to river confluences, whereas patterns of productivity (yearling cubs per km(2)) and adult female density (individuals per km(2)) were associated with more general measures of habitat quality and areas of shelter in poor habitat. This suggests that density may not accurately distinguish between high-quality 'source' areas and low-quality sites that merely provide refuges for effectively non-reproductive individuals. Our results indicate that density may be a misleading indicator of real estate value, particularly for populations that do not conform to an ideal free distribution.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".