Background As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. Methods In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period. Findings Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8–15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3–21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio [OR] 0·23 [95% CI 0·11–0·46]) and among adults aged 20–59 years (OR 0·64 [95% CI 0·43–0·97]). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 [95% CI 0·27–1·38]). The estimated local reproductive number ( R ) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41–0·62) in Guangzhou. The projected local R , had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49–0·74) when household was defined on the basis of close relatives. Interpretation SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contac...

Rates of ecosystem processes such as decomposition are likely to change as a result of human impacts on the environment. In southern California, climate change and nitrogen (N) deposition in particular may alter biological communities and ecosystem processes. These drivers may affect decomposition directly, through changes in abiotic conditions, and indirectly through changes in plant and decomposer communities. To assess indirect effects on litter decomposition, we reciprocally transplanted microbial communities and plant litter among control and treatment plots (either drought or N addition) in a grassland ecosystem. We hypothesized that drought would reduce decomposition rates through moisture limitation of decomposers and reductions in plant litter quality before and during decomposition. In contrast, we predicted that N deposition would stimulate decomposition by relieving N limitation of decomposers and improving plant litter quality. We also hypothesized that adaptive mechanisms would allow microbes to decompose litter more effectively in their native plot and litter environments. Consistent with our first hypothesis, we found that drought treatment reduced litter mass loss from 20.9% to 15.3% after six months. There was a similar decline in mass loss of litter inoculated with microbes transplanted from the drought treatment, suggesting a legacy effect of drought driven by declines in microbial abundance and possible changes in microbial community composition. Bacterial cell densities were up to 86% lower in drought plots and at least 50% lower on litter derived from the drought treatment, whereas fungal hyphal lengths increased by 13-14% in the drought treatment. Nitrogen effects on decomposition rates and microbial abundances were weaker than drought effects, although N addition significantly altered initial plant litter chemistry and litter chemistry during decomposition. However, we did find support for microbial adaptation to N addition with N-derived microbes facilitating greater mass loss in N plots than in control plots. Our results show that environmental changes can affect rates of ecosystem processes directly through abiotic changes and indirectly through microbial abundances and communities. Therefore models of ecosystem response to global change may need to represent microbial biomass and community composition to make accurate predictions.

Peer reviewed eScholarship.org Powered by the California Digital Library University of California Highlights We measured drought and N impact on litter enzyme activities and microbial biomass. Despite semi-arid conditions, the litter was bacterially dominated. Enzyme efficiencies declined with drought. Enzyme efficiencies in the N treatment suggest microbes adapt to local environment. Measuring enzyme efficiencies could help predict in-situ enzyme activity. a b s t r a c t Microbial enzymes play a fundamental role in ecosystem processes and nutrient mineralization. Therefore understanding enzyme responses to anthropogenic environmental change is important for predicting ecosystem function in the future. In a previous study, we used a reciprocal transplant design to examine the direct and indirect effects of drought and nitrogen (N) fertilization on litter decomposition in a southern California grassland. This work showed direct and indirect negative effects of drought on decomposition, and faster decomposition by N-adapted microbial communities in N-fertilized plots than in non-fertilized plots. Here we measured microbial biomass and the activities of nine extracellular enzymes to examine the microbial and enzymatic mechanisms underlying litter decomposition responses to drought and N. We hypothesized that changes in fungal biomass and potential extracellular enzyme activity (EEA) would relate directly to litter decomposition responses. We also predicted that fungal biomass would dominate the microbial community in our semi-arid study site. However, we found that the microbial community was dominated by bacterial biomass, and that bacteria responded negatively to drought treatment. In contrast to patterns in decomposition, fungal biomass and most potential EEA increased in direct response to drought treatment. Potential EEA was also decoupled from the decomposition response to N treatment. These results suggest that drought and N alter the effi-ciencies of EEA, defined as the mass of target substrate lost per unit potential EEA. Enzyme efficiencies declined with drought treatment, possibly because reduced water availability increased enzyme immobilization and reduced diffusion rates. In the N experiment, the efficiencies of b-glucosidase, b-xylosidase, and polyphenol oxidase were greater when microbes were transplanted into environments from which they originated. This increase in enzymatic efficiency suggests that microbial enzymes may adapt to their local environment. Overall, our results indicate that drought and N addition may have predictable impacts on the efficiencies of extracellular enzymes, providing a means of linking enzyme potentials with in-situ activities.

Terrestrial ecosystem models assume that microbial communities respond instantaneously, or are immediately resilient, to environmental change. Here we tested this assumption by quantifying the resilience of a leaf litter community to changes in precipitation or nitrogen availability. By manipulating composition within a global change experiment, we decoupled the legacies of abiotic parameters versus that of the microbial community itself. After one rainy season, more variation in fungal composition could be explained by the original microbial inoculum than the litterbag environment (18% versus 5.5% of total variation). This compositional legacy persisted for 3 years, when 6% of the variability in fungal composition was still explained by the microbial origin. In contrast, bacterial composition was generally more resilient than fungal composition. Microbial functioning (measured as decomposition rate) was not immediately resilient to the global change manipulations; decomposition depended on both the contemporary environment and rainfall the year prior. Finally, using metagenomic sequencing, we showed that changes in precipitation, but not nitrogen availability, altered the potential for bacterial carbohydrate degradation, suggesting why the functional consequences of the two experiments may have differed. Predictions of how terrestrial ecosystem processes respond to environmental change may thus be improved by considering the legacies of microbial communities.

The high diversity of microbial communities hampers predictions about their responses to global change. Here we investigate the potential for using a phylogenetic, trait-based framework to capture the response of bacteria and fungi to global change manipulations. Replicated grassland plots were subjected to 3+ years of drought and nitrogen fertilization. The responses of leaf litter bacteria and fungi to these simulated changes were significantly phylogenetically conserved. Proportional changes in abundance were highly correlated among related organisms, such that relatives with approximately 5% ribosomal DNA genetic distance showed similar responses to the treatments. A microbe's change in relative abundance was significantly correlated between the treatments, suggesting a compromise between numerical abundance in undisturbed environments and resistance to change in general, independent of disturbance type. Lineages in which at least 90% of the microbes shared the same response were circumscribed at a modest phylogenetic depth (τ D 0.014-0.021), but significantly larger than randomized simulations predict. In several clades, phylogenetic depth of trait consensus was higher. Fungal response to drought was more conserved than was response to nitrogen fertilization, whereas bacteria responded equally to both treatments. Finally, we show that a bacterium's response to the manipulations is correlated with its potential functional traits (measured here as the number of glycoside hydrolase genes encoding the capacity to degrade different types of carbohydrates). Together, these results suggest that a phylogenetic, trait-based framework may be useful for predicting shifts in microbial composition and functioning in the face of global change.