Managing animal movement conserves predator–prey dynamics
Bibliographic record
Abstract
Anthropogenic landscape change can lead to novel interactions among species with consequences for predator–prey dynamics. Population responses of predators and prey vary according to species abundance, movement, and distribution. Although conservation often involves managing species abundance, culling predators to benefit vulnerable prey is socially, ethically, and ecologically tenuous. We conducted a before–after control–impact experiment to test how well mitigations designed to disrupt animal movement reduce encounters between endangered caribou (Rangifer tarandus) and their predators in a human‐modified landscape. Using camera traps, we quantified coincident habitat use by predators and prey. By deploying obstacles to disrupt ease of movement on human developments, we reduced the ratio of wolf (Canis lupus)–caribou encounters by 85% and reduced the ratio of black bear (Ursus americanus)–caribou encounters by 60%, without removing predators. By managing animal movements that regulate predator–prey encounters, risk to endangered species can be reduced without the disruptive trophic effects caused by intensive carnivore removals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".