Dynamics of infection and immunity over 50 years as marine stickleback adapt to freshwater
Bibliographic record
Abstract
Abstract When a species colonizes a new environment, it may encounter new parasites to which its immune system is poorly adapted. After an initial spike in infection rates in the naïve founder population, the host may subsequently evolve increased immunity, thereby reducing infection rates. Here, we present an example of this eco-evolutionary process in a population of threespine stickleback (Gasterosteus aculeatus) that was founded in Heisholt Quarry, a man-made quarry pond, in 1967. Marine stickleback rarely encounter Schistocephalus solidus tapeworms (which require freshwater to hatch), and so remain highly susceptible to infection. Initially, introduced marine fish were heavily infected by S. solidus. They exhibited low levels of fibrosis, a heritable immune trait that some genotypes activate in response to infection, thereby suppressing tapeworm growth and viability. By the 1990s, the Heisholt Quarry population exhibited high rates of fibrosis, which partly suppressed S. solidus infection. This increased immune response led to reduced infection rates, and the tapeworm was apparently extirpated by 2021. Because fibrosis has a strong genetic basis in other stickleback populations, we infer that the newly founded stickleback–parasite interaction exhibits an eco-evolutionary process of increased immunity that effectively reduced infection. The infection and immune dynamics documented here closely match those expected from a simple eco-evo dynamic model presented here.
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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 | 0.000 |
| 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) | 0.001 | 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".