Testing for dual impacts of contaminants and parasites on hosts: the importance of skew
Bibliographic record
Abstract
A review of recent studies published over a 23-year timespan (1990–2012) showed rapidly increasing interest in exploring how environmental contaminants and parasitism might influence each other and (or) interact to affect host health. Those experimental and observational studies fall into three broad categories (comparative studies of the possible influence of each factor on the other, correlative studies between contaminants and parasitism, and studies on relative bioaccumulation of contaminants by parasites versus their hosts). Despite the exponential increase in relevant studies, little attention has been paid to how contaminants and parasitism should co-occur among individuals within host populations and (or) how the nature of co-distributions should be incorporated into study designs and analyses. Null expectations of co-distributions between contaminants and parasitism can be derived from underlying distributions of each factor. Using a subset of studies, we found contaminant distributions showed positive skew in about one third of cases testing for correlations between contaminant concentrations and parasitism among hosts. We show such skew is expected for theoretical reasons. We used this information to guide simulations wherein the oft-cited negative binomial distribution of parasitism (also supported by theory) was combined with both log-normal (skewed) and normal distributions of contaminants to generate expected null co-distributions. Simulations demonstrated an increasingly concave (or L-shaped) co-distribution with increasing contaminant positive skew: proportionately more individuals experience low levels of each factor while few to none experienced high contaminant and high parasite burdens simultaneously. Our results have the following implications: they call into question experimental studies exposing specimens to parasites and pollutants at levels higher than, or even equal to, observed averages, and they provide a framework for exploring how individual-based effects might scale up into effects at the population level. Potential improvements to study designs and (or) statistical tests are offered that recognize the need to understand the underlying distributions of both contaminants and parasitism and the degree to which one can infer host population effects.
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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 | 0.124 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".