Anthropic pressure drives resource selection of an adaptable generalist in human‐dominated landscapes
Bibliographic record
Abstract
Abstract Few landscapes on earth remain free of human‐modification, which may influence resource selection in wildlife. To investigate the effects of anthropic pressure on wild boar ( Sus scrofa ) and explore management implications, we studied how diel resource selection of the species' main life stages changed with spatial variations of human access (e.g., for recreation), temporal changes in hunting pressure, and habitat type. Using 206,461 hourly GPS‐locations of 15 males, 11 females with dependent young, and 17 other females from south‐western Germany, we found anthropic pressure influenced resource selection more than ecological factors. All boars were more likely to select for low human‐access areas than high human‐access areas, regardless of habitat. Hunting pressure was most avoided by females with dependent piglets, followed by males and other females. Since both hunting activity and general human access affected resource selection, they should be considered simultaneously in wildlife management and conservation. We suggest the further establishment of wildlife reserves that are inaccessible to people where boar may remain more localized, thereby reducing the risk of disease transmission, and boar hunting to focus on open lands and refuge boundaries to reduce crop damage. This may also benefit overall human‐wildlife coexistence, animal welfare, and biodiversity conservation in anthropized environments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".