Spatial Ecology and Predator-Prey Interactions of Mountain Lions in California's San Francisco North Bay
Bibliographic record
Abstract
Understanding spatial ecology and predator-prey interactions are central to wildlife ecology and conservation. We tracked mountain lions with GPS telemetry to evaluate space use, predator-prey interactions, and resource selection in California’s San Francisco North Bay, USA. In Chapter 1, we evaluated the influence of human disturbance on home range size, prey composition, and kill rates on black-tailed deer (Odocoileus hemionus columbianus) across an urban-rural gradient. Both males and females increased home range size with increasing development at low to moderate levels of development, but female home range size stabilized across greater proportions of development while male home range size decreased, possibly due to constrained movement. Deer kill rates, prey composition, and time spent at kills did not vary relative to human infrastructure or natural landscape features relevant to predation (e.g., cover, productivity). The rate at which mountain lions killed deer in the North Bay was generally comparable to estimates reported across North America. In Chapter 2, we investigated scale-dependent responses by mountain lions to their primary limiting factors along a gradient of human disturbance. Mountain lions exhibited flexible strategies by selecting home ranges in both more and less developed areas. Within home ranges, responses to human infrastructure were highly variable as a function of both distance from tree cover and the amount of human infrastructure present. In Chapter 3, we evaluated resource selection by mountain lions at locations where they consumed black-tailed deer, their primary prey. Mountain lions strongly selected tree and shrub cover at feeding sites. However, their selection of primary productivity increased as a function of cover, indicating that mountain lions exhibited the strongest selection of areas with vegetative features where prey were likely both abundant and vulnerable. Our work highlights the significant overlap between mountain lions and people in the North Bay as mountain lions killed and consumed their primary prey surprisingly close to buildings (mean = 373 m). Mountain lions are highly adaptable and respond flexibly to human disturbance as some aspects of their behavior varied strongly relative to human disturbance (e.g., space use, resource selection), whereas other aspects appeared to be more consistent (e.g., predation). Advisor: John F. Benson
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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 | 0.001 | 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".