<i>Ebolavirus</i> evolution and emergence are associated with land use change
Bibliographic record
Abstract
Abstract Anthropogenic land use change facilitates disease emergence by altering the interface between humans and pathogen reservoirs and is hypothesized to drive pathogen evolution. Here, we show a positive association between land use change and the evolution and dispersal of Zaire ebolavirus (EBOV) and Sudan ebolavirus (SUDV). We update the phylogeographies of EBOV and SUDV, which reveal that the most recent common ancestor of EBOV was circulating around 1960 in the forests of what is now the northwestern Democratic Republic of the Congo, while the most recent common ancestor of SUDV was circulating around 1958 in the southern Sudanese savanna. Both landscapes underwent significant anthropogenic fragmentation between 1940 and 1960, associated with specific colonial “schemes,” which substantially altered local human settlement patterns and the surrounding vegetation to support intensive cash crop agriculture. Since these disturbances, landscape fragmentation was spatiotemporally associated with the divergence and dispersal of new variants of both viruses into new ecoregions of Africa. These variants segregated geographically along ecoregion boundaries, resembling a pattern observable for other bat‐borne viruses. The amino acid changes which characterized each variant disproportionately involved glycosylation‐sensitive amino acids in the surface glycoprotein domain responsible for immune evasion and attachment to host cells, suggesting adaptation to new hosts amidst changing landscapes. Our results show that land use change not only increases the risk of spillover, but also impacts the evolution of viruses themselves.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".