Host specificity and the reproductive strategies of parasites
Bibliographic record
Abstract
Environmental stability can have profound impacts on life history trait evolution in organisms, especially with respect to development and reproduction. In theory, free-living species, when subjected to relatively stable and predictable conditions over many generations, should evolve narrow niche breadths and become more specialized. In parasitic organisms, this level of specialization is reflected by their host specificity. Here, we tested how host specificity impacts the reproductive strategies of parasites, a subject seldomly addressed for this group. Through an extensive review of the literature, we collated a worldwide dataset to predict, through Bayesian multilevel modelling, the effect of host specificity on the reproductive strategies of parasitic copepods of fishes or corals. We found that copepods of fishes with low host specificity (generalists) invest more into reproductive output with larger clutch sizes, whereas generalist copepods of corals invest less into reproductive output with smaller clutch sizes. The differences in host turnover rates through an evolutionary timescale could explain the contrasting strategies across species observed here, which should still favour the odds of parasites encountering and infecting a host. Ultimately, the differences found in this study reflect the unique evolutionary history that parasites share both intrinsically and extrinsically with their hosts.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".