Local adaptation and host specificity to copepod intermediate hosts by the tapeworm <i>Schistocephalus solidus</i>
Bibliographic record
Abstract
Abstract Host–parasite coevolution may lead to patterns of local adaptation in either the host or parasite. For parasites with complex multi‐host life cycles, this coevolution may be more challenging as they must adapt to multiple geographically varying hosts. The tapeworm Schistocephalus solidus exhibits some local adaptation to its second intermediate host, threespine stickleback, to which the parasite is strictly specialized. However, the tapeworm's adaptation to its first intermediate host (any of a number of copepod species) is not documented. We investigated if there was local adaptation and host specify in the tapeworm Schistocephalus solidus to its copepod first intermediate hosts. We exposed copepods from five lakes in Vancouver Island (BC, Canada) to local (i.e. same lake) and foreign tapeworms in a reciprocal exposure experiment. Results indicate that the tapeworm is not locally adapted to the copepods. Instead, we observed moderate‐effect host specificity, infection rates being higher in certain copepod species than in others. Infection rates also varied among cestode populations. These results show that although S. solidus infects multiple copepod genera, they are not equally competent hosts. Differences in S. solidus epidemiology among lakes is likely to be driven more by this partial specialization, than by local adaptation to first intermediate 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 | 0.000 | 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".