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Abstract The Household Pulse Survey (HPS), released by the US Census Bureau at the start of the coronavirus pandemic, gathers timely information about the societal and economic impacts of coronavirus. The first phase of the survey was launched in April 2020 and ran for 12 weeks. To track the immediate impact of the pandemic, individual respondents during this phase were re-sampled for up to three consecutive weeks. Motivated by expected job loss during the pandemic, using public-use microdata, this work proposes unit-level, model-based estimators that incorporate longitudinal dependence at both the response and domain level. In particular, using a pseudo-likelihood, we consider a Bayesian hierarchical unit-level, model-based approach for both Gaussian and binary response data under informative sampling. To facilitate construction of these model-based estimates, we develop an efficient Gibbs sampler. An empirical simulation study is conducted to compare the proposed approach to models that do not account for unit-level longitudinal correlation. Finally, using public-use HPS micro-data, we provide an analysis of ‘expected job loss’ that compares both design- and model-based estimators and demonstrates superior performance for the proposed model-based approaches. 

Water monitoring at four locations on the Choptank River and four locations on the Pocomoke River in Maryland, U.S.A., was conducted from 2021 through 2023. Funding and scientific rationale were provided by the National Science Foundation grant 2049073 (“Resolving Sediment Connectivity between Rivers and Estuaries by Tracking Particles with their Microbial Genetic Signature”). The monitoring locations were chosen to measure estuary dynamics from the tidal freshwater zone through the mesohaline estuary. Parameters measured included water temperature, water level, water conductivity (reported as specific conductivity), water turbidity, and water velocity. 

Diet has been found to significantly influence gut microbiota throughout various life stages, and gut microbiota have been increasingly shown to influence host physiology, health, and behavior. This study uses 16S rRNA sequencing to examine the effects of six different fat-supplemented diets (canola oil, coconut oil, fish oil, flaxseed oil, lard, and olive oil) on broiler chicken cecal microbial composition and predicted function in comparison with a common and inexpensive fat source (poultry fat). Groups of broilers were fed each of these diets and then evaluated on day 41 and day 55 of age. For both 41- and 55-day samples,FirmicutesandBacteroidetesphyla were the dominant bacteria in the ceca accounting for 99% of the microbial community. Across the 41- and 55-day samples, treatment time was associated with a stronger and more significant microbiota shift (p < 0.001) than differences in dietary treatment alone (p = 0.117), but dietary treatment combined with treatment time is a significant factor as well (p = 0.047). Sparse partial least squares discriminant analysis was used to explore the more discriminating taxa for each treatment group. For identified species, butyrate production appears to be affected in a diet-specific manner, with many butyrate-producing species being evident for the fish-based diet at day 41 and a few of these species for the flaxseed-based diet at day 55. Predicted functions, as conducted with PICRUSt2, were significant for comparisons between the control and the flaxseed-based dietary treatment group at day 55, with indications of host health benefit for the flaxseed-based diet. Predicted functions found to be significant were for enzymes and pathways such as propionate CoA ligase, aminobutyraldehyde dehydrogenase, vitamin B12-transporting ATPase, thiamine kinase, acetylneuraminate epimerase, and L-tryptophan biosynthesis. This study provides insight surrounding specific dietary fat-based treatments to be investigated further and highlights the importance of polyunsaturated fat sources in poultry feed that may offer a favorable cecal microbial modulation compared to saturated fat sources. 

Infectious disease is a major driver of biodiversity loss, but how disease threatens pollinator communities remains poorly understood. Here, we review the plant–pollinator–pathogen literature to identify mechanisms by which plant and pollinator traits and community composition influence pathogen transmission and assess consequences of transmission on plant and pollinator fitness. We find that plant and pollinator traits that increase floral contact can amplify transmission, but community-level factors such as plant and pollinator abundance are often correlated and can counteract one another. Although disease reduces pollinator fitness in some species, little research has assessed cascading effects on pollination, and taxonomic representation outside of honey bees and bumble bees remains poor. Major open challenges include (a) disentangling correlations between plant and pollinator abundance to understand how community composition impacts pathogen transmission and (b) distinguishing when pathogen transmission results in disease. Addressing these issues, as well as expanding taxonomic representation of pollinators, will deepen our understanding of how pathogens impact diverse pollinator communities. 

Watershed sediment can increase elevation of tidal wetlands struggling against rising seas, but where and how much watershed sediment helps is unknown. By combining contiguous US datasets on sediment loads and tidal wetland areas for 4972 rivers and their estuaries, we calculated that river sediment accretion will be insufficient to match sea level rise in 72% of cases because most watersheds are too small (median 21 square kilometers) to generate adequate sediment. Nearly half the tidal wetlands would require 10 times more river sediment to match sea level, a magnitude not generally achievable by dam removal in some regions. The realization that watershed sediment has little effect on most tidal wetland elevations shifts research priorities toward biological processes and coastal sediment dynamics that most influence elevation change.