The Antarctic and Arctic regions offer a unique opportunity to test factors shaping biogeography of marine microbial communities because these regions are geographically far apart, yet share similar selection pressures. Here, we report a comprehensive comparison of bacterioplankton diversity between polar oceans, using standardized methods for pyrosequencing the V6 region of the small subunit ribosomal (SSU) rRNA gene. Bacterial communities from lower latitude oceans were included, providing a global perspective. A clear difference between Southern and Arctic Ocean surface communities was evident, with 78% of operational taxonomic units (OTUs) unique to the Southern Ocean and 70% unique to the Arctic Ocean. Although polar ocean bacterial communities were more similar to each other than to lower latitude pelagic communities, analyses of depths, seasons, and coastal vs. open waters, the Southern and Arctic Ocean bacterioplankton communities consistently clustered separately from each other. Coastal surface Southern and Arctic Ocean communities were more dissimilar from their respective open ocean communities. In contrast, deep ocean communities differed less between poles and lower latitude deep waters and displayed different diversity patterns compared with the surface. In addition, estimated diversity (Chao1) for surface and deep communities did not correlate significantly with latitude or temperature. Our results suggest differences in environmental conditions at the poles and different selection mechanisms controlling surface and deep ocean community structure and diversity. Surface bacterioplankton may be subjected to more short-term, variable conditions, whereas deep communities appear to be structured by longer water-mass residence time and connectivity through ocean circulation. bipolar | biodiversity | next-generation sequencing | microbial ecology

Despite the high abundance of Archaea in the global ocean, their metabolism and biogeochemical roles remain largely unresolved. We investigated the population dynamics and metabolic activity of Thaumarchaeota in polar environments, where these microorganisms are particularly abundant and exhibit seasonal growth. Thaumarchaeota were more abundant in deep Arctic and Antarctic waters and grew throughout the winter at surface and deeper Arctic halocline waters. However, in situ single-cell activity measurements revealed a low activity of this group in the uptake of both leucine and bicarbonate (<5% Thaumarchaeota cells active), which is inconsistent with known heterotrophic and autotrophic thaumarchaeal lifestyles. These results suggested the existence of alternative sources of carbon and energy. Our analysis of an environmental metagenome from the Arctic winter revealed that Thaumarchaeota had pathways for ammonia oxidation and, unexpectedly, an abundance of genes involved in urea transport and degradation. Quantitative PCR analysis confirmed that most polar Thaumarchaeota had the potential to oxidize ammonia, and a large fraction of them had urease genes, enabling the use of urea to fuel nitrification. Thaumarchaeota from Arctic deep waters had a higher abundance of urease genes than those near the surface suggesting genetic differences between closely related archaeal populations. In situ measurements of urea uptake and concentration in Arctic waters showed that small-sized prokaryotes incorporated the carbon from urea, and the availability of urea was often higher than that of ammonium. Therefore, the degradation of urea may be a relevant pathway for Thaumarchaeota and other microorganisms exposed to the low-energy conditions of dark polar waters.amoA | ureC | Beaufort Sea | Ross Sea | Amundsen Sea

More than a century of ecological studies have demonstrated the importance of demography in shaping spatial and temporal variation in population dynamics. Surprisingly, the impact of seasonal recruitment on infectious disease systems has received much less attention. Here, we present data encompassing 78 years of monthly natality in the USA, and reveal pronounced seasonality in birth rates, with geographical and temporal variation in both the peak birth timing and amplitude. The timing of annual birth pulses followed a latitudinal gradient, with northern states exhibiting spring/summer peaks and southern states exhibiting autumn peaks, a pattern we also observed throughout the Northern Hemisphere. Additionally, the amplitude of United States birth seasonality was more than twofold greater in southern states versus those in the north. Next, we examined the dynamical impact of birth seasonality on childhood disease incidence, using a mechanistic model of measles. Birth seasonality was found to have the potential to alter the magnitude and periodicity of epidemics, with the effect dependent on both birth peak timing and amplitude. In a simulation study, we fitted an susceptible-exposed-infectedrecovered model to simulated data, and demonstrated that ignoring birth seasonality can bias the estimation of critical epidemiological parameters. Finally, we carried out statistical inference using historical measles incidence data from New York City. Our analyses did not identify the predicted systematic biases in parameter estimates. This may be owing to the well-known frequency-locking between measles epidemics and seasonal transmission rates, or may arise from substantial uncertainty in multiple model parameters and estimation stochasticity.

Public health surveillance systems are important for tracking disease dynamics. In recent years, social and real-time digital data sources have provided new means of studying disease transmission. Such affordable and accessible data have the potential to offer new insights into disease epidemiology at national and international scales. We used the extensive information repository Google Trends to examine the digital epidemiology of a common childhood disease, chicken pox, caused by varicella zoster virus (VZV), over an 11-y period. We (i) report robust seasonal information-seeking behavior for chicken pox using Google data from 36 countries, (ii) validate Google data using clinical chicken pox cases, (iii) demonstrate that Google data can be used to identify recurrent seasonal outbreaks and forecast their magnitude and seasonal timing, and (iv) reveal that VZV immunization significantly dampened seasonal cycles in informationseeking behavior. Our findings provide strong evidence that VZV transmission is seasonal and that seasonal peaks show remarkable latitudinal variation. We attribute the dampened seasonal cycles in chicken pox information-seeking behavior to VZV vaccine-induced reduction of seasonal transmission. These data and the methodological approaches provide a way to track the global burden of childhood disease and illustrate population-level effects of immunization. The global latitudinal patterns in outbreak seasonality could direct future studies of environmental and physiological drivers of disease transmission.

Traditional influenza surveillance informs control strategies but can lag behind outbreak onset and undercount cases. Wastewater surveillance is effective for monitoring near real-time dynamics of outbreaks but has not been attempted for influenza. We quantified influenza A virus (IAV) RNA in wastewater during two active outbreaks on university campuses in different parts of the United States and during different times of year using case data from an outbreak investigation and high-quality surveillance data from student athletes. In both cases, the IAV RNA concentrations were strongly associated with reported IAV incidence rates (Kendall's τ values of 0.58 and 0.67 for the University of Michigan and Stanford University, respectively). Furthermore, the RNA concentrations reflected outbreak patterns and magnitudes. For the University of Michigan outbreak, evidence from sequencing IAV RNA from wastewater indicated the same circulating strain identified in cases during the outbreak. The results demonstrate that wastewater surveillance can effectively detect influenza outbreaks and will therefore be a valuable supplement to traditional forms of influenza surveillance.

We compared SARS-CoV-2 RNA concentrations in primary settled solids and raw wastewater samples matched in date to investigate the relationship between the two matrices.

Vaccines that autonomously transfer among individuals have been proposed as a strategy to control infectious diseases within inaccessible wildlife populations. However, rates of vaccine spread and epidemiological efficacy in real-world systems remain elusive. Here, we investigate whether topical vaccines that transfer among individuals through social contacts can control vampire bat rabies-a medically and economically important zoonosis in Latin America. Field experiments in three Peruvian bat colonies, which used fluorescent biomarkers as a proxy for the bat-to-bat transfer and ingestion of an oral vaccine, revealed that vaccine transfer would increase population-level immunity up to 2.6 times beyond the same effort using conventional, nonspreadable vaccines. Mathematical models showed that observed levels of vaccine transfer would reduce the probability, size and duration of rabies outbreaks, even at low but realistically achievable levels of vaccine application. Models further predicted that existing vaccines provide substantial advantages over culling bats-the policy currently implemented in North, Central and South America. Linking field studies with biomarkers to mathematical models can inform how spreadable vaccines may combat pathogens of health and conservation concern before costly investments in vaccine design and testing.

Traditional influenza surveillance informs control strategies but can lag behind outbreak onset and undercount cases, while wastewater surveillance is effective for monitoring near real-time dynamics of outbreaks but has not been attempted for influenza. We quantified Influenza A virus RNA in wastewater during an active outbreak. RNA concentrations were strongly associated with reported clinical cases and reflected the outbreak pattern and magnitude, suggesting this approach could aid in early detection.