Global Priority Conservation Areas in the Face of 21st Century Climate Change
Bibliographic record
Abstract
In an era when global biodiversity is increasingly impacted by rapidly changing climate, efforts to conserve global biodiversity may be compromised if we do not consider the uneven distribution of climate-induced threats. Here, via a novel application of an aggregate Regional Climate Change Index (RCCI) that combines changes in mean annual temperature and precipitation with changes in their interannual variability, we assess multi-dimensional climate changes across the "Global 200" ecoregions - a set of priority ecoregions designed to "achieve the goal of saving a broad diversity of the Earth's ecosystems" - over the 21(st) century. Using an ensemble of 62 climate scenarios, our analyses show that, between 1991-2010 and 2081-2100, 96% of the ecoregions considered will be likely (more than 66% probability) to face moderate-to-pronounced climate changes, when compared to the magnitudes of change during the past five decades. Ecoregions at high northern latitudes are projected to experience most pronounced climate change, followed by those in the Mediterranean Basin, Amazon Basin, East Africa, and South Asia. Relatively modest RCCI signals are expected over ecoregions in Northwest South America, West Africa, and Southeast Asia, yet with considerable uncertainties. Although not indicative of climate-change impacts per se, the RCCI-based assessment can help policy-makers gain a quantitative and comprehensive overview of the unevenly distributed climate risks across the G200 ecoregions. Whether due to significant climate change signals or large uncertainties, the ecoregions highlighted in the assessment deserve special attention in more detailed impact assessments to inform effective conservation strategies under future climate change.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".