Host demography influences the prevalence and severity of eelgrass wasting disease
Bibliographic record
Abstract
Many marine pathogens are opportunists, present in the environment, but causing disease only under certain conditions such as immunosuppression due to environmental stress or host factors such as age. In the temperate eelgrass Zostera marina, the opportunistic labyrinthulomycete pathogen Labyrinthula zosterae is present in many populations and occasionally causes severe epidemics of wasting disease; however, risk factors associated with these epidemics are unknown. We conducted both field surveys and experimental manipulations to examine the effect of leaf age (inferred from leaf size) on wasting disease prevalence and severity in Z. marina across sites in the San Juan Archipelago, Washington, USA. We confirmed that lesions observed in the field were caused by active Labyrinthula infections both by identifying the etiologic agent through histology and by performing inoculations with cultures of Labyrinthula spp. isolated from observed lesions. We found that disease prevalence increased at shallower depths and with greater leaf size at all sites, and this effect was more pronounced at declining sites. Experimental inoculations with 2 strains of L. zosterae confirmed an increased susceptibility of older leaves to infection. Overall, this pattern suggests that mature beds and shallow beds of eelgrass may be especially susceptible to outbreaks of wasting disease. The study highlights the importance of considering host and environmental factors when evaluating risk of disease from opportunistic pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".