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Raina, Jean-Baptiste (Ed.)ABSTRACT Predicting outcomes of marine disease outbreaks presents a challenge in the face of both global and local stressors. Host-associated microbiomes may play important roles in disease dynamics but remain understudied in marine ecosystems. Host–pathogen–microbiome interactions can vary across host ranges, gradients of disease, and temperature; studying these relationships may aid our ability to forecast disease dynamics. Eelgrass, Zostera marina , is impacted by outbreaks of wasting disease caused by the opportunistic pathogen Labyrinthula zosterae . We investigated how Z. marina phyllosphere microbial communities vary with rising wasting disease lesion prevalence and severity relative to plant and meadow characteristics like shoot density, longest leaf length, and temperature across 23° latitude in the Northeastern Pacific. We detected effects of geography (11%) and smaller, but distinct, effects of temperature (30-day max sea surface temperature, 4%) and disease (lesion prevalence, 3%) on microbiome composition. Declines in alpha diversity on asymptomatic tissue occurred with rising wasting disease prevalence within meadows. However, no change in microbiome variability (dispersion) was detected between asymptomatic and symptomatic tissues. Further, we identified members of Cellvibrionaceae, Colwelliaceae, and Granulosicoccaceae on asymptomatic tissue that are predictive of wasting disease prevalence across the geographic range (3,100 kilometers). Functional roles of Colwelliaceae andmore »Free, publicly-accessible full text available August 30, 2023
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Free, publicly-accessible full text available July 1, 2023
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Seagrass meadows provide valuable ecosystem benefits but are at risk from disease. Eelgrass ( Zostera marina ) is a temperate species threatened by seagrass wasting disease (SWD), caused by the protist Labyrinthula zosterae . The pathogen is sensitive to warming ocean temperatures, prompting a need for greater understanding of the impacts on host health under climate change. Previous work demonstrates pathogen cultures grow faster under warmer laboratory conditions and documents positive correlations between warmer ocean temperatures and disease levels in nature. However, the consequences of disease outbreaks on eelgrass growth remain poorly understood. Here, we examined the effect of disease on eelgrass productivity in the field. We coupled in situ shoot marking with high-resolution imagery of eelgrass blades and used an artificial intelligence application to determine disease prevalence and severity from digital images. Comparisons of eelgrass growth and disease metrics showed that SWD impaired eelgrass growth and accumulation of non-structural carbon in the field. Blades with more severe disease had reduced growth rates, indicating that disease severity can limit plant growth. Disease severity and rhizome sugar content were also inversely related, suggesting that disease reduced belowground carbon accumulation. Finally, repeated measurements of diseased blades indicated that lesions can grow fastermore »
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Abstract In studies of the unicellular eukaryote Dictyostelium discoideum , many have anecdotally observed that cell dilution below a certain ‘threshold density’ causes cells to undergo a period of slow growth (lag). However, little is documented about the slow growth phase and the reason for different growth dynamics below and above this threshold density. In this paper, we extend and correct our earlier work to report an extensive set of experiments, including the use of new cell counting technology, that set this slow-to-fast growth transition on a much firmer biological basis. We show that dilution below a certain density (around 10 4 cells ml −1 ) causes cells to grow slower on average and exhibit a large degree of variability: sometimes a sample does not lag at all, while sometimes it takes many moderate density cell cycle times to recover back to fast growth. We perform conditioned media experiments to demonstrate that a chemical signal mediates this endogenous phenomenon. Finally, we argue that while simple models involving fluid transport of signal molecules or cluster-based signaling explain typical behavior, they do not capture the high degree of variability between samples but nevertheless favor an intra-cluster mechanism.Free, publicly-accessible full text available January 31, 2023
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Computational advances reveal opportunities for more sustainable hydropower development in large transboundary river basins.Free, publicly-accessible full text available February 18, 2023