Extinctions Caused by Host-Range Expansion
Bibliographic record
Abstract
Abstract. Nearly all emerging diseases in humans are a result of host-range expansion, in which a pathogen of one species evolves the ability to infect a new host species. To present a rigorous analysis of pathogen host-range expansion, we derive a Lotka–Volterra dynamical system with two competing host species and a single parasite species; the parasite infects only one of the host species. We provide a stability and bifurcation analysis of this model. We then ask what happens if the parasite evolves the ability to infect the alternate host, extending the model to include a parasite population with an expanded host range. We derive explicit global stability and bifurcation conditions for this four-dimensional model in terms of the system parameters. We demonstrate that only four outcomes may occur following the range expansion of a parasite or pathogen, and provide both local and global asymptotic stability conditions for these outcomes. While three of these outcomes were expected, the fourth is counterintuitive, predicting that host-range expansion can drive the original host species to extinction. For example, a native species could be driven to extinction by a longstanding native parasite if that parasite acquires the ability to infect a cultivated species. We briefly discuss the phenomena driving this unexpected prediction and its implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".