Marine viruses are critical drivers of ocean biogeochemistry and their abundances vary spatiotemporally in the global oceans, with upper estimates exceeding 10 8 per ml. Over many years, a consensus has emerged that virus abundances are typically 10-fold higher than prokaryote abundances. The use of a fixed-ratio suggests that the relationship between virus and prokaryote abundances is both predictable and linear. However, the true explanatory power of a linear relationship and its robustness across diverse ocean environments is unclear. Here, we compile 5671 prokaryote and virus abundance estimates from 25 distinct marine surveys to characterize the relationship between virus and prokaryote abundances. We find that the median virus-to-prokaryote ratio (VPR) is 10:1 and 16:1 in the near-and sub-surface oceans, respectively. Nonetheless, we observe substantial variation in the VPR and find either no or limited explanatory power using fixed-ratio models. Instead, virus abundances are better described as nonlinear, power-law functions of prokaryote abundances -particularly when considering relationships within distinct marine surveys. Estimated power-laws have scaling exponents that are typically less than 1, signifying that the VPR decreases with prokaryote density, rather than remaining fixed. The emergence of power-law scaling presents a challenge for mechanistic models seeking to understand the ecological causes and consequences of marine virusmicrobe interactions. Such power-law scaling also implies that efforts to average viral effects on microbial mortality and biogeochemical cycles using "representative" abundances or abundanceratios need to be refined if they are to be utilized to make quantitative predictions at regional or global ocean scales.

Viral lysis of microbial hosts releases organic matter that can then be assimilated by nontargeted microorganisms. Quantitative estimates of virus-mediated recycling of carbon in marine waters, first established in the late 1990s, were originally extrapolated from marine host and virus densities, host carbon content and inferred viral lysis rates. Yet, these estimates did not explicitly incorporate the cascade of complex feedbacks associated with virus-mediated lysis. To evaluate the role of viruses in shaping community structure and ecosystem functioning, we extend dynamic multitrophic ecosystem models to include a virus component, specifically parameterized for processes taking place in the ocean euphotic zone. Crucially, we are able to solve this model analytically, facilitating evaluation of model behavior under many alternative parameterizations. Analyses reveal that the addition of a virus component promotes the emergence of complex communities. In addition, biomass partitioning of the emergent multitrophic community is consistent with well-established empirical norms in the surface oceans. At steady state, ecosystem fluxes can be probed to characterize the effects that viruses have when compared with putative marine surface ecosystems without viruses. The model suggests that ecosystems with viruses will have (1) increased organic matter recycling, (2) reduced transfer to higher trophic levels and (3) increased net primary productivity. These model findings support hypotheses that viruses can have significant stimulatory effects across whole-ecosystem scales. We suggest that existing efforts to predict carbon and nutrient cycling without considering virus effects are likely to miss essential features of marine food webs that regulate global biogeochemical cycles.

Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.

Ecological systems can change substantially in response to small shifts in environmental conditions. Such changes are characterized by a non‐linear relationship between the value of the response variable and one or more explanatory variables. Documenting the magnitude of change and the environmental conditions that give rise to these threshold responses is important for both the scientific community and the agencies charged with ecosystem management. A threshold is defined as a substantial change in a response variable, given a marginal change in environmental conditions. Here, we demonstrate the usefulness of a derivative‐based method for detecting ecological thresholds along a single explanatory variable. The “significant zero crossings” (SiZer) approach uses a non‐parametric method to approximate the response function and its derivatives and then examines how those functions change across the range of the explanatory variable. SiZer makes fewer assumptions than conventional threshold models and explores a full range of smoothing functions. We believe SiZer is a useful technique for the exploratory analysis of many ecological datasets.

In this study we examined changes to the human gut microbiome resulting from an eight-week intervention of either cardiorespiratory exercise (CRE) or resistance training exercise (RTE). Twenty-eight subjects (21 F; aged 18–26) were recruited for our CRE study and 28 subjects (17 F; aged 18–33) were recruited for our RTE study. Fecal samples for gut microbiome profiling were collected twice weekly during the pre-intervention phase (three weeks), intervention phase (eight weeks), and post-intervention phase (three weeks). Pre/post VO2max, three repetition maximum (3RM), and body composition measurements were conducted. Heart rate ranges for CRE were determined by subjects’ initial VO2max test. RTE weight ranges were established by subjects’ initial 3RM testing for squat, bench press, and bent-over row. Gut microbiota were profiled using 16S rRNA gene sequencing. Microbiome sequence data were analyzed with QIIME 2. CRE resulted in initial changes to the gut microbiome which were not sustained through or after the intervention period, while RTE resulted in no detectable changes to the gut microbiota. For both CRE and RTE, we observe some evidence that the baseline microbiome composition may be predictive of exercise gains. This work suggests that the human gut microbiome can change in response to a new exercise program, but the type of exercise likely impacts whether a change occurs. The changes observed in our CRE intervention resemble a disturbance to the microbiome, where an initial shift is observed followed by a return to the baseline state. More work is needed to understand how sustained changes to the microbiome occur, resulting in differences that have been reported in cross sectional studies of athletes and non-athletes.