A marine subsidy reshapes the ecology of a large terrestrial carnivore
Bibliographic record
Abstract
Efforts to restore wildlife populations are increasing worldwide, yet many of these initiatives take place amidst significant ecological change. In altered ecosystems with novel species compositions, returning wildlife may alter their behaviours and interactions with other species. In Patagonia, the extirpation of terrestrial predators following European colonization, including the puma, facilitated the establishment of Magellanic penguin colonies along Argentina's coast. Recently, the creation of a national park in coastal Patagonia has fostered the recovery of pumas and resulted in an unexpected predator-prey relationship between pumas and penguins. Using a suite of animal movement metrics and generalized spatial mark-resight models, we tested how access to penguins affects puma behaviour and abundance. Consistent with the resource dispersion hypothesis, pumas responded to the availability and abundance of penguins by increasing their site fidelity when penguins were present but ranging more widely when the penguins migrated out of the park. This behavioural adaptation led to frequent encounters among pumas, suggesting greater social tolerance. Further, the park now supports the highest density of pumas recorded to date. Our work adds to a rapidly growing body of literature suggesting that the restoration of large carnivores in novel ecosystems can lead to novel interactions that transform their behaviours.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".