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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 (Lacunaspp.) 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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Information-incorporated clustering analysis of disease prevalence trends
In biomedical research the analysis of disease prevalence is of critical importance. While most of the existing prevalence studies focus on individual diseases, there has been increasing effort that jointly examines the prevalence values and their trends of multiple diseases. Such joint analysis can provide valuable insights not shared by individual-disease analysis. A critical limitation of the existing analysis is that there is a lack of attention to existing information, which has been accumulated through a large number of studies and can be valuable especially when there are a large number of diseases but the number of prevalence values for a specific disease is limited. In this study we conduct the functional clustering analysis of prevalence trends for a large number of diseases. A novel approach based on the penalized fusion technique is developed to incorporate information mined from published articles. It is innovatively designed to take into account that such information may not be fully relevant or correct. Another significant development is that statistical properties are rigorously established. Simulation is conducted and demonstrates its competitive performance. In the analysis of data from Taiwan NHIRD (National Health Insurance Research Database), new and interesting findings that differ from the existing ones are made.
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- PAR ID:
- 10512845
- Publisher / Repository:
- Institute of Mathematical Statistics
- Date Published:
- Journal Name:
- The Annals of Applied Statistics
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1932-6157
- Page Range / eLocation ID:
- 1035–1050
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1214/23-AOAS1821
