Northern protected areas will become important refuges for biodiversity tracking suitable climates
Bibliographic record
Abstract
Abstract The Northern Biodiversity Paradox predicts that, despite its globally negative effects on biodiversity, climate change will increase biodiversity in northern regions where many species are limited by low temperatures. We assessed the potential impacts of climate change on the biodiversity of a northern network of 1,749 protected areas spread over >600,000 km2 in Quebec, Canada. Using ecological niche modeling, we calculated potential changes in the probability of occurrence of 529 species to evaluate the potential impacts of climate change on (1) species gain, loss, turnover, and richness in protected areas, (2) representativity of protected areas, and (3) extent of species ranges located in protected areas. We predict a major species turnover over time, with 49% of total protected land area potentially experiencing a species turnover >80%. We also predict increases in regional species richness, representativity of protected areas, and species protection provided by protected areas. Although we did not model the likelihood of species colonising habitats that become suitable as a result of climate change, northern protected areas should ultimately become important refuges for species tracking climate northward. This is the first study to examine in such details the potential effects of climate change on a northern protected area network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".