Scaling up from epidemiology to biogeography: local infection patterns predict geographical distribution in fish parasites
Bibliographic record
Abstract
Abstract Aim We investigated how the spatial distribution of parasites, measured as either their geographical range size or their frequency of occurrence among localities, relates to either their average local abundance or the variance in their abundance among localities where they occur. Location We used data on the abundance of 46 metazoan parasite species in 66 populations of threespine sticklebacks, Gasterosteus aculeatus , from Europe and North America. Methods For each parasite species, frequency of occurrence was calculated as the proportion of stickleback populations in which it occurred, and geographical range size as the area within the smallest possible polygon delimited using the coordinates of the localities where it occurred. Generalized linear models were used to assess how these two measures of spatial distribution were influenced by several predictor variables: geographical region (North America or Europe), life cycle (simple or complex), average local abundance, the coefficient of variation in abundance across localities, and median prevalence (proportion of infected hosts within a locality). Results Our analyses uncovered four patterns. First, parasites in North America tend to have higher frequencies of occurrence among surveyed localities, but not broader geographical ranges, than those in Europe. Second, parasite species with simple life cycles have wider geographical ranges than those with complex cycles. Third, there was a positive relationship between average abundance of the different parasite species and their frequency of occurrence, but not between average abundance and geographical range size. Fourth, the coefficient of variation in abundance covaried positively with both the frequency of occurrence and geographical range size across the different parasite species. Thus, all else being equal, parasites showing greater site‐to‐site variability in abundance occur in a greater proportion of localities and over a broader geographical area than those with a more stable abundance among sites. Main conclusions Local infection patterns are linked with large‐scale distributional patterns in fish parasites, independently of host effects, such that local commonness translates into regional commonness. The mechanisms linking parasite success at both scales remain unclear, but may include those that maintain the continuum between specialist and generalist parasites. Regardless, the observed patterns have implications for the predicted changes in the geographical distributions of many parasites in response to climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".