Functional Connectivity of the World’s Protected Areas
Bibliographic record
Abstract
Abstract Rapid environmental change threatens to isolate the world’s wildlife populations and intensify biodiversity loss. Global policies have called for expanding and connecting the world’s protected areas (PAs) to curtail the crisis, yet how well PA networks currently support wildlife movement, and where connectivity conservation or restoration is most critical, have never been mapped globally. Here, we map the functional connectivity (how animals move through landscapes) of the world’s terrestrial PAs for the first time. Also, going beyond existing global connectivity indices, we quantify national PA-connectedness using an approach that meaningfully represents animal movement through anthropogenic landscapes. We find that reducing the human footprint may improve national PA-connectivity more than adding new PAs; however, both strategies are critical for improving and preserving connectivity in places where the predicted flow of animal movement is highly concentrated. We show that the majority of critical connectivity areas (CCAs) (defined as globally important areas of concentrated animal movements) remain unprotected. Of these, 72% overlap with previously-identified global conservation priority areas, while 3% of CCAs occur within moderate to heavily modified lands. Conservation and restoration of CCAs could safeguard connectivity of the world’s PAs, and dovetail with previously identified global conservation priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".