Bibliographic record
Abstract
Earth's terrestrial large carnivores form a highly endangered group of species with unique conservation challenges. The majority of these species have experienced major geographical range contractions, which puts many of them at high risk of extinction or of becoming ecologically ineffective. As a result of these range contractions and the associated loss of intact predator guilds, the ecological effects of these species are now far less widespread and common, with inevitable consequences for ecosystem function. Rewilding—which includes reintroducing species into portions of their former ranges—is an important carnivore conservation tool and means for restoring top-down ecological regulation. We conducted a global analysis of potential reintroduction areas. We first considered protected areas where one or more large carnivore species have been extirpated, identifying a total of 130 protected areas that may be most suitable for carnivore reintroduction. These protected areas include sites in every major world region, and are most commonly found in Mongolia ( n = 13), Canada ( n = 11), Thailand ( n = 9), Namibia ( n = 6), Indonesia ( n = 6) and Australia ( n = 6). We considered the sizes of protected areas, their levels of protection, the extent of human impacts within and around the protected areas, and the status of prey species in the protected areas. Finally, we used the ‘last of the wild’ approach to identify contiguous low human footprint regions within the former ranges of each species, identifying an additional 150 areas which could be the focus of conservation efforts to create conditions conducive to reintroductions. These low footprint regions were most commonly found in the USA ( n = 14), Russia ( n = 14), Canada ( n = 10), China ( n = 9) and Mauritania ( n = 8). Together, our results show the global-scale potential for carnivore rewilding projects to both conserve these species and provide critical ecological and social benefits.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".