Host traits and temperature predict biogeographical variation in seagrass disease prevalence
Bibliographic record
Abstract
Diseases are ubiquitous in natural systems, with broad effects across populations, communities and ecosystems. However, the drivers of many diseases remain poorly understood, particularly in marine environments, inhibiting effective conservation and management measures. We examined biogeographical patterns of infection in the foundational seagrass Zostera marina by the parasitic protist Labyrinthula zosterae , the causative agent of seagrass wasting disease, across >20° of latitude in two ocean basins. We then identified and characterized relationships among wasting disease prevalence and a suite of host traits and environmental variables. Host characteristics and transmission dynamics explained most of the variance in prevalence across our survey, yet the particular host traits underlying these relationships varied between oceans, with host size and nitrogen content important in the Pacific and host size and density most important in the Atlantic. Temperature was also a key predictor of prevalence, particularly in the Pacific Ocean. The strength and shape of the relationships between prevalence and some predictors differed in our large-scale survey versus previous experimental and site-specific work. These results show that both host characteristics and environment influence host–parasite interactions, and that some such effects scale up predictably, whereas others appear to depend on regional or local context.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".