Niche‐based host extinction increases prevalence of an environmentally acquired pathogen
Bibliographic record
Abstract
Understanding the ecology of environmentally acquired and multi‐host pathogens affecting humans and wildlife has been elusive in part because fluctuations in the abundance of host and pathogen species may feed back onto pathogen transmission. Complexity of pathogen‐host dynamics emerges from processes driving local extinction of the pathogen, its hosts and non‐hosts. While the extinction of species may entail losses in pathogen–host interactions and decrease the proportion of hosts infected by a pathogen (prevalence), some studies suggest the opposite pattern. Niche‐based extinction, based on the species tolerance to environmental conditions, may increase prevalence of infection because the pathogen and its hosts persist, while other species go extinct. Hence, understanding prevalence of infection requires disentangling random‐ and niche‐based extinction processes occurring simultaneously. To contribute to this exercise, we analysed the prevalence of an environmentally acquired, multi‐host pathogen, Mycobacterium ulcerans (MU), in a unique dataset of 16 communities of freshwater animals, surveyed during 12 months in Akonolinga, Cameroon in equatorial Africa. Two different ecosystems were identified: rivers (lotic) and swamps and flooded areas (lentic). Increased prevalence of MU infection was correlated with niche‐based extinction of aquatic host invertebrates and vertebrates in the lentic ecosystems, whereas decreased prevalence was associated with random disassembly of the lotic ecosystems. This finding suggests that random and niche‐based extinction of host taxa are key to assessing the effect of local extinction of species on the ecology of environmentally acquired and multi‐host pathogens.
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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) | 0.005 | 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 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".