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  1. Free, publicly-accessible full text available June 1, 2024
  2. Wild waterbirds, and especially wild waterfowl, are considered to be a reservoir for avian influenza viruses, with transmission likely occurring at the agricultural-wildlife interface. In the past few decades, avian influenza has repeatedly emerged in China along the East Asian-Australasian Flyway (EAAF), where extensive habitat conversion has occurred. Rapid environmental changes in the EAAF, especially distributional changes in rice paddy agriculture, have the potential to affect both the movements of wild migratory birds and the likelihood of spillover at the agricultural-wildlife interface. To begin to understand the potential implications such changes may have on waterfowl and disease transmission risk, we created dynamic Brownian Bridge Movement Models (dBBMM) based on waterfowl telemetry data. We used these dBBMM models to create hypothetical scenarios that would predict likely changes in waterfowl distribution relative to recent changes in rice distribution quantified through remote sensing. Our models examined a range of responses in which increased availability of rice paddies would drive increased use by waterfowl and decreased availability would result in decreased use, predicted from empirical data. Results from our scenarios suggested that in southeast China, relatively small decreases in rice agriculture could lead to dramatic loss of stopover habitat, and in northeast China, increases in rice paddies should provide new areas that can be used by waterfowl. Finally, we explored the implications of how such scenarios of changing waterfowl distribution may affect the potential for avian influenza transmission. Our results provide advance understanding of changing disease transmission threats by incorporating real-world data that predicts differences in habitat utilization by migratory birds over time. 
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    Free, publicly-accessible full text available May 1, 2024
  3. Abstract

    Sustainable biofuel cropping systems aim to address climate change while meeting energy needs. Understanding how soil and plant‐associated microbes respond to these different cropping systems is key to promoting agriculture sustainability and evaluating changes in ecosystem functions. Here, we leverage a long‐term biofuel cropping system field experiment to dissect soil and root microbiome changes across a soil‐depth gradient in poplar, restored prairie and switchgrass to understand their effects on the microbial communities. High throughput amplicon sequencing of the fungal internal transcribed spacer (ITS) and prokaryotic 16S DNA regions showed a common trend of root and soil microbial community richness decreasing and evenness increasing with depth. Ecological niche (root vs. soil) had the strongest effect on community structure, followed by depth, then crop. Stochastic processes dominated the structuring of fungal communities in deeper soil layers while operational taxonomic units (OTUs) in surface soil layers were more likely to co‐occur and to be enriched by plant hosts. Prokaryotic communities were dispersal limited at deeper depths. Microbial networks showed a higher density, connectedness, average degree and module size in deeper soils. We observed a decrease in fungal‐fungal links and an increase of bacteria–bacteria links with increasing depth in all crops, particularly in the root microbiome.

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  4. The process of fermenting tofu extends back thousands of years and is an indispensable part of Chinese culture. Despite a cultural resurgence in fermented foods and interest in microbiomes, there is little knowledge on the microbial diversity represented in fermented ‘hairy’ tofu, known locally in China as Mao tofu. High-throughput metagenomic sequencing of the ITS, LSU and 16S rDNA was used to determine Mao tofu’s fungal and bacterial community diversity across four wet markets in Yunnan, China. The results show that hairy tofu in this region consists of around 170 fungal and 365 bacterial taxa, and that microbial taxa differ between markets. Diversity also differed based on the specific niche of the tofu block, comparing the outside rind-like niche to that of the inside of the tofu block. Machine learning random forest models were able to accurately classify both the market and niche of sample origin. An over-abundance of yeast and Geotrichum was found, and Mucor (Mucoromycota) was abundant in the outside rind-like niche, which consists of the visible ‘hairy’ mycelium. The majority of the bacterial OTUs belonged to Proteobacteria, Firmicutes, and Bacteroidetes, with Acinetobacter, Lactobacillus, Sphingobacterium and Flavobacterium the most abundant genera. Putative fungal pathogens of plants (Cercospora, Diaporthe, Fusarium) and animals (Metarhizium, Entomomortierella, Pyxidiophora, Candida, Clavispora) were also detected, as were putative bacterial pathogens identified as Legionella. Non-fungal eukaryotic taxa detected by LSU amplicon sequencing included soybean (Glycine max), Protozoa, Metazoa (e.g., Nematoda and Platyhelminthes), Rhizaria and Chromista, indicating that additional biodiversity exists in the hairy tofu microbiome. 
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