Productivity and fishing pressure drive variability in fish parasite assemblages of the Line Islands, equatorial Pacific
Bibliographic record
Abstract
Variability in primary productivity and fishing pressure can shape the abundance, species composition, and diversity of marine life. Though parasites comprise nearly half of marine species, their responses to these important forces remain little explored. We quantified parasite assemblages at two spatial scales, across a gradient in productivity and fishing pressure that spans six coral islands of the Line Islands archipelago and within the largest Line Island, Kiritimati, which experiences a west-to-east gradient in fishing pressure and upwelling-driven productivity. In the across-islands data set, we found that increasing productivity was correlated with increased parasite abundance overall, but that the effects of productivity differed among parasite groups. Trophically transmitted parasites increased in abundance with increasing productivity, but directly transmitted parasites did not exhibit significant changes. This probably arises because productivity has stronger effects on the abundance of the planktonic crustaceans and herbivorous snails that serve as the intermediate hosts of trophically transmitted parasites than on the higher-trophic level fishes that are the sole hosts of directly transmitted parasites. We also found that specialist parasites increased in response to increasing productivity, while generalists did not, possibly because specialist parasites tend to be more strongly limited by host availability than are generalist parasites. After the effect of productivity was controlled for, fishing was correlated with decreases in the abundance of trophically transmitted parasites, while directly transmitted parasites appeared to track host density; we observed increases in the abundance of parasites using hosts that experienced fishing-driven compensatory increases in abundance. The within-island data set confirmed these patterns for the combined effects of productivity and fishing on parasite abundance, suggesting that our conclusions are robust across a span of spatial scales. Overall, these results indicate that there are strong and variable effects of anthropogenic and natural drivers on parasite abundance and taxonomic richness. These effects are likely to be mediated by parasite traits, particularly by parasite transmission strategies.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".