Black bear (<i>Ursus americanus</i>) functional resource selection relative to intraspecific competition and human risk
Bibliographic record
Abstract
The spatial scales at which animals make behavioral trade-offs is assumed to relate to the scales at which factors most limiting resources and increasing mortality risk occur. We used global positioning system collar locations of 29 reproductive-age female black bears (Ursus americanus Pallas, 1780) in three states to assess resource selection relative to bear population-specific density, an index of vegetation productivity, riparian corridors, or two road classes of and within home ranges during spring–summer of 2009–2013. Female resource selection was best explained by functional responses to vegetation productivity across nearly all populations and spatial scales, which appeared to be influenced by variation in bear density (i.e., intraspecific competition). Behavioral trade-offs were greatest at the landscape scale, but except for vegetation productivity, were consistent for populations across spatial scales. Females across populations selected locations nearer to tertiary roads, but females in Michigan and Mississippi selected main roads and avoided riparian corridors, whereas females in Missouri did the opposite, suggesting population-level trade-offs between resource (e.g., food) acquisition and mortality risks (e.g., vehicle collisions). Our study emphasizes that female bear population-level resource selection can be influenced by multiple spatially dependent factors, and that scale-dependent functional behavior should be identified for management of bears across their range.
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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 | 0.000 | 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".