Seagrass wasting disease prevalence and lesion area increase with invertebrate grazing across the northeastern Pacific
Bibliographic record
Abstract
Disease is a key driver of community and ecosystem structure, especially when it strikes foundation species. In the widespread marine foundation species eelgrass (Zostera marina), outbreaks of wasting disease have caused large-scale meadow collapse in the past, and the causative pathogen, Labyrinthula zosterae, is commonly found in meadows globally. Research to date has mainly focused on abiotic environmental drivers of seagrass wasting disease, but there is strong evidence from other systems that biotic interactions such as herbivory can facilitate plant diseases. How biotic interactions influence seagrass wasting disease in the field is unknown but is potentially important for understanding dynamics of this globally valuable and declining habitat. Here, we investigated links between epifaunal grazers and seagrass wasting disease using a latitudinal field study across 32 eelgrass meadows distributed from southeastern Alaska to southern California. From 2019 to 2021, we conducted annual surveys to assess eelgrass shoot density, morphology, epifauna community, and the prevalence and lesion area of wasting disease infections. We integrated field data with satellite measurements of sea surface temperature and used structural equation modeling to test the magnitude and direction of possible drivers of wasting disease. Our results show that grazing by small invertebrates was associated with a 29% increase in prevalence of wasting disease infections and that both the prevalence and lesion area of disease increased with total epifauna abundances. Furthermore, these relationships differed among taxa; disease levels increased with snail (Lacuna spp.) and idoteid isopod abundances but were not related to abundance of ampithoid amphipods. This field study across 23° of latitude suggests a prominent role for invertebrate consumers in facilitating disease outbreaks with potentially large impacts on coastal seagrass ecosystems.
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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 | 0.001 | 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".