Microbiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.

Intestinal microbiota perform many functions for their host, but among the most important is their role in metabolism, especially the conversion of recalcitrant biomass that the host is unable to digest into bioavailable compounds. Most studies have focused on the assistance gut microbiota provide in the metabolism of carbohydrates, however, their role in host amino acid metabolism is poorly understood. We conducted an experiment on Mus musculus using 16S rRNA gene sequencing and carbon isotope analysis of essential amino acids (AA ESS ) to quantify the community composition of gut microbiota and the contribution of carbohydrate carbon used by the gut microbiome to synthesize AA ESS that are assimilated by mice to build skeletal muscle tissue. The relative abundances of Firmicutes and Bacteroidetes inversely varied as a function of dietary macromolecular content, with Firmicutes dominating when mice were fed low-protein diets that contained the highest proportions of simple carbohydrates (sucrose). Mixing models estimated that the microbial contribution of AA ESS to mouse muscle varied from less than 5% (threonine, lysine, and phenylalanine) to approximately 60% (valine) across diet treatments, with the Firmicute-dominated microbiome associated with the greatest contribution. Our results show that intestinal microbes can provide a significant source of the AA ESS their host uses to synthesize structural tissues. The role that gut microbiota play in the amino acid metabolism of animals that consume protein-deficient diets is likely a significant but under-recognized aspect of foraging ecology and physiology.

The distribution of organisms in an environment is neither uniform nor random but is instead spatially patterned. The factors that control this patterning are complex and the underlying mechanisms are poorly understood. Soil microbes are critical to ecosystem function but exhibit highly complex distributions and community dynamics due in large part to the scale-dependent effects of environmental heterogeneity. To better understand the impact of environmental heterogeneity on the distribution of soil microbes, we sequenced the 16S rRNA gene from bacterial communities in the microbe-dominated polar desert ecosystem of the McMurdo Dry Valleys (MDV), Antarctica. Significant differences in key edaphic variables and alpha diversity were observed among the three lake basins of the Taylor Valley (Kruskal–Wallis; pH: χ2 = 68.89, P < 0.001, conductivity: χ2 = 35.03, P < 0.001, observed species: χ2 = 7.98, P = 0.019 and inverse Simpson: χ2 = 18.52, P < 0.001) and each basin supported distinctive microbial communities (ANOSIM R = 0.466, P = 0.001, random forest ratio of 14.1). However, relationships between community structure and edaphic characteristics were highly variable and contextual, ranging in magnitude and direction across regional, basin, and local scales. Correlations among edaphic factors (pH and soil conductivity) and the relative abundance of specific phyla were most pronounced along local environmental gradients in the Lake Fryxell basin where Acidobacteria, Bacteroidetes, and Proteobacteria declined while Deinococcus–Thermus and Gemmatimonadetes increased with soil conductivity (all P < 0.1). Species richness was most strongly related to the soil conductivity gradient present within this study system. We suggest that the relative importance of pH versus soil conductivity in structuring microbial communities is related to the length of edaphic gradients and the spatial scale of sampling. These results highlight the importance of conducting studies over large ranges of key environmental gradients and across multiple spatial scales to assess the influence of environmental heterogeneity on the composition and diversity of microbial communities.

To date, the potential impact of viral communities on biogeochemical cycles in soil has largely been inferred from correlational evidence, such as virus-driven changes in microbial abundances, viral auxiliary metabolic genes, and links with soil physiochemical properties. To more directly test the impact of soil viruses on carbon cycling during plant litter decomposition, we added concentrated viral community suspensions to complex litter decomposer communities in 40-day microcosm experiments. Microbial communities from two New Mexico alpine soils, Pajarito (PJ) and Santa Fe (SF), were inoculated onto grass litter on sand, and three treatments were applied in triplicate to each set of microcosms: addition of buffer (no added virus), live virus (+virus), or killed-virus (+killed-virus) fractions extracted from the same soil. Significant differences in respiration were observed between the +virus and +killed-virus treatments in the PJ, but not the SF microcosms. Bacterial and fungal community composition differed significantly by treatment in both PJ and SF microcosms. Combining data across both soils, viral addition altered links between bacterial and fungal diversity, dissolved organic carbon and total nitrogen. Overall, we demonstrate that increasing viral pressure in complex microbial communities can impact terrestrial biogeochemical cycling but is context-dependent.

Rationale Ecologists increasingly determine the δ15N values of amino acids (AA) in animal tissue; “source” AA typically exhibit minor variation between diet and consumer, while “trophic” AA have increased δ15N values in consumers. Thus, trophic‐source δ15N offsets (i.e., Δ15NT‐S) reflect trophic position in a food web. However, even minor variations in δ15Nsource AA values may influence the magnitude of offset that represents a trophic step, known as the trophic discrimination factor (i.e., TDFT‐S). Diet digestibility and protein content can influence the δ15N values of bulk animal tissue, but the effects of these factors on AA Δ15NT‐S and TDFT‐S in mammals are unknown. Methods We fed captive mice (Mus musculus) either (A) a low‐fat, high‐fiber diet with low, intermediate, or high protein; or (B) a high‐fat, low‐fiber diet with low or intermediate protein. Mouse muscle and dietary protein were analyzed for bulk tissue δ15N using elemental analyzer‐isotope ratio mass spectrometry (EA‐IRMS), and were also hydrolyzed into free AA that were analyzed for δ15N using gas chromatography‐combustion‐IRMS. Results As dietary protein increased, Δ15NConsumer‐Diet slightly declined for bulk muscle tissue in both experiments; increased for AA in the low‐fat, high‐fiber diet (A); and remained the same or decreased for AA in the high‐fat, low‐fiber diet (B). The effects of dietary protein on Δ15NT‐S and on TDFT‐S varied by AA but were consistent between variables. Conclusions Diets were less digestible and included more protein in Experiment A than in Experiment B. As a result, the mice in Experiment A probably oxidized more AA, resulting in greater Δ15NConsumer‐Diet values. However, the similar responses of Δ15NT‐S and of TDFT‐S to diet variation suggest that if diet samples are available, Δ15NT‐S accurately tracks trophic position. If diet samples are not available, the patterns presented here provide a basis to interpret Δ15NT‐S values. The trophic‐source offset of Pro‐Lys did not vary across diets, and therefore may be more reliable for omnivores than other offsets (e.g., Glu‐Phe).