Evolutionary rescue accelerates competitive exclusion in a parasite community
Bibliographic record
Abstract
Abstract Environmental stress drives biodiversity loss by altering competitive hierarchies and pushing taxa towards extinction. Parasites and their communities are particularly vulnerable to stress due to environmental sensitivity of infection steps, variation in species tolerance during co-infections, and dependence on host fitness. Parasite populations might avoid extinction through evolutionary rescue – whereby rapid adaptation to stress enables persistence – but whether this process can preserve community diversity remains unclear. Here, we study the impact of evolutionary rescue in a simple parasite community by propagating populations of two viral parasites (bacteriophages ϕ14-1 and ϕLUZ19) of Pseudomonas aeruginosa in monoculture and co-culture under two thermal conditions, a control temperature (37°C) and a high temperature that restricts ϕ14-1 growth (42°C). We show that evolutionary rescue of ϕ14-1 prevented extinction in monoculture. Rescue of this phage in co-culture made it a superior competitor, and it replaced ϕLUZ19 as the dominant phage at high temperature. We determine that evolutionary rescue occurred through mutations in genes linked to attachment to bacterial hosts and within-host replication. We also show that competitive suppression by ϕ14-1 constrained ϕLUZ19 molecular evolution. Our findings suggest that evolutionary rescue can prevent the extinction of some parasites, but may inadvertently destabilise the community and facilitate further biodiversity loss. This work underscores the need to take an eco-evolutionary approach to predict the responses of communities to global 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".