Lesser snow goose helminths show recurring and positive parasite infection-diversity relations
Bibliographic record
Abstract
The patterns and mechanisms by which biological diversity is associated with parasite infection risk are important to study because of their potential implications for wildlife population's conservation and management.Almost all research in this area has focused on host species diversity and has neglected parasite diversity, despite evidence that parasites are important drivers of community structure and ecosystem processes.Here, we assessed whether presence or abundance of each of nine helminth species parasitizing lesser snow geese (Chen caerulescens) was associated with indices of parasite diversity (i.e.species richness and Shannon's Diversity Index).We found repeated instances of focal parasite presence and abundance having significant positive co-variation with diversity measures of other parasites.These results occurred both within individual samples and for combinations of all samples.Whereas host condition and parasite facilitation could be drivers of the patterns we observed, other host-or parasitelevel effects, such as age or sex class of host or taxon of parasite, were discounted as explanatory variables.Our findings of recurring and positive associations between focal parasite abundance and diversity underscore the importance of moving beyond pairwise species interactions and contexts, and of including the oft-neglected parasite species diversity in infection-diversity studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".