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Plant-associated microbial assemblages are known to shift at time scales aligned with plant phenology, as influenced by the changes in plant-derived nutrient concentrations and abiotic conditions observed over a growing season. But these same factors can change dramatically in a sub-24-hour period, and it is poorly understood how such diel cycling may influence plant-associated microbiomes. Plants respond to the change from day to night via mechanisms collectively referred to as the internal “clock,” and clock phenotypes are associated with shifts in rhizosphere exudates and other changes that we hypothesize could affect rhizosphere microbes. The mustardBoechera strictahas wild populations that contain multiple clock phenotypes of either a 21- or a 24-hour cycle. We grew plants of both phenotypes (two genotypes per phenotype) in incubators that simulated natural diel cycling or that maintained constant light and temperature. Under both cycling and constant conditions, the extracted DNA concentration and the composition of rhizosphere microbial assemblages differed between time points, with daytime DNA concentrations often triple what were observed at night and microbial community composition differing by, for instance, up to 17%. While we found that plants of different genotypes were associated with variation in rhizosphere assemblages, we did not see an effect on soil conditioned by a particular host plant circadian phenotype on subsequent generations of plants. Our results suggest that rhizosphere microbiomes are dynamic at sub-24-hour periods, and those dynamics are shaped by diel cycling in host plant phenotype.

The rhizosphere microbiome influences many aspects of plant fitness, including production of secondary compounds and defence against insect herbivores. Plants also modulate the composition of the microbial community in the rhizosphere via secretion of root exudates. We tested both the effect of the rhizosphere microbiome on plant traits, and host plant effects on rhizosphere microbes using recombinant inbred lines (RILs) ofBrassica rapathat differ in production of glucosinolates (GLS), secondary metabolites that contribute to defence against insect herbivores. First, we investigated the effect of genetic variation in GLS production on the composition of the rhizosphere microbiome. Using a Bayesian Dirichlet‐multinomial regression model (DMBVS), we identified both negative and positive associations between bacteria from six genera and the concentration of five GLS compounds produced in plant roots. Additionally, we tested the effects of microbial inoculation (an intact vs. disrupted soil microbiome) on GLS production and insect damage in these RILs. We found a significant microbial treatment × genotype interaction, in which total GLS was higher in the intact relative to the disrupted microbiome treatment in some RILs. However, despite differences in GLS production between microbial treatments, we observed no difference in insect damage between treatments. Together, these results provide evidence for a full feedback cycle of plant–microbe interactions mediated by GLS; that is, GLS compounds produced by the host plant “feed‐down” to influence rhizosphere microbial community and rhizosphere microbes “feed‐up” to influence GLS production.

Circadian clocks confer adaptation to predictable 24‐h fluctuations in the exogenous environment, but it has yet to be determined what ecological factors maintain natural genetic variation in endogenous circadian period outside of the hypothesized optimum of 24 h. We estimated quantitative genetic variation in circadian period in leaf movement in 30 natural populations of theArabidopsisrelativeBoechera strictasampled within only 1° of latitude but across an elevation gradient spanning 2460–3300 m in the Rocky Mountains. Measuring ~3800 plants from 473 maternal families (7–20 per population), we found that genetic variation was of similar magnitude among versus within populations, with population means varying between 21.9 and 24.9 h and maternal family means within populations varying by up to ~6 h. After statistically accounting for spatial autocorrelation at a habitat extreme, we found that elevation explained a significant proportion of genetic variation in the circadian period, such that higher‐elevation populations had shorter mean period lengths and reduced intrapopulation ranges. Environmental data indicate that these spatial trends could be related to steep regional climatic gradients in temperature, precipitation, and their intra‐annual variability. Our findings suggest that spatially fine‐grained environmental heterogeneity contributes to naturally occurring genetic variation in circadian traits in wild populations.

Endophytes are microbes that live, for at least a portion of their life history, within plant tissues. Endophyte assemblages are often composed of a few abundant taxa and many infrequently observed, low-biomass taxa that are, in a word, rare. The ways in which most endophytes affect host phenotype are unknown; however, certain dominant endophytes can influence plants in ecologically meaningful ways—including by affecting growth and immune system functioning. In contrast, the effects of rare endophytes on their hosts have been unexplored, including how rare endophytes might interact with abundant endophytes to shape plant phenotype. Here, we manipulate both the suite of rare foliar endophytes (including both fungi and bacteria) and Alternaria fulva–a vertically transmitted and usually abundant fungus–within the fabaceous forb Astragalus lentiginosus. We report that rare, low-biomass endophytes affected host size and foliar %N, but only when the heritable fungal endophyte (A. fulva) was not present. A. fulva also reduced plant size and %N, but these deleterious effects on the host could be offset by a negative association we observed between this heritable fungus and a foliar pathogen. These results demonstrate how interactions among endophytic taxa determine the net effects on host plants and suggest that the myriad rare endophytes within plant leaves may be more than a collection of uninfluential, commensal organisms, but instead have meaningful ecological roles.

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet‐multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM—Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

Certain general facets of biotic response to climate change, such as shifts in phenology and geographic distribution, are well characterized; however, it is not clear whether the observed similarity of responses across taxa will extend to variation in other population‐level processes. We examined population response to climatic variation using long‐term incidence data (collected over 42 years) encompassing 149 butterfly species and considerable habitat diversity (10 sites along an elevational gradient from sea level to over 2,700 m in California). Population responses were characterized by extreme heterogeneity that was not attributable to differences in species composition among sites. These results indicate that habitat heterogeneity might be a buffer against climate change and highlight important questions about mechanisms maintaining interpopulation differences in responses to weather. Despite overall heterogeneity of response, population dynamics were accurately predicted by our model for many species at each site. However, the overall correlation between observed and predicted incidence in a cross validation analysis was moderate (Pearson'sr = 0.23,SE0.01), and 97% of observed data fell within the predicted 95% credible intervals. Prediction was most successful for more abundant species as well as for sites with lower annual turnover. Population‐level heterogeneity in response to climate variation and the limits of our predictive power highlight the challenges for a future of increasing climatic variability.

Different methods are commonly used to assign core microbiome membership, leading to methodological inconsistencies across studies. In this study, we review a set of the most commonly used core microbiome assignment methods and compare their core assignments using both simulated and empirical data.

The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques.