Data from: Whether larval amphibians school does not affect the parasite aggregation rule: testing the effects of host spatial heterogeneity in field and experimental studies
Bibliographic record
Abstract
Almost all macroparasites show over-dispersed infections within natural host populations such that most parasites are distributed among a few heavily-infected individuals. Despite the importance of parasite aggregation for understanding system stability, the potential for population regulation, and super-spreading events, many questions persist about its underlying drivers. Theoretically, aggregation results from heterogeneity in host exposure, resistance, and tolerance. However, few studies have examined how host spatial arrangement – which likely affects both parasite encounter and density-dependent interactions – influences infection and dispersion, representing a critical gap in our current knowledge regarding the possible drivers of parasite aggregation. Using field data from over 165 ponds and 8,000 hosts, we evaluated how the spatial clustering of amphibian larvae within ponds 1) varied among different amphibian species, and 2), affected the distribution of parasites within the host population using Taylor’s Power Law. A complementary mesocosm experiment used field-guided manipulations of the spatial arrangement of larval amphibians to create a gradient in host clustering while controlling host density, thereby testing for spatial effects on both infection success and aggregation by three different trematode species. Our field data indicated that larval amphibians exhibited significant spatial clustering that was well captured by Taylor’s Power Law (R2 0.92 to 0.97 for different host species), but the residual variation only weakly correlated with observed patterns of trematode parasite over-dispersion. Correspondingly, experimental manipulation of host clustering had no effects on parasite infection success or the degree of parasite aggregation among cages or mesocosms. Given the importance of parasite over-dispersion for host populations and disease dynamics, we advocate for further investigations of host and parasite spatial aggregation, particularly studies that incorporate and/or control for heterogeneity in exposure and susceptibility.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".