Systemic enrichment of antifungal traits in the rhizosphere microbiome after pathogen attack

1. Plant-associated microbial communities are crucial for plant growth and play an important role in disease suppression. Community composition and function change upon pathogen attack, yet to date, we do not know whether these changes are a side effect of the infection or actively driven by the plant. 2. Here, we used a split-root approach to test whether barley plants recruit bacteria carrying antifungal traits upon infestation with Fusarium graminearum. Split-root systems allow disentangling local infection effects, such as root damage, from systemic, plant-driven effects on microbiome functionality. We assessed the recruitment of fluorescent pseudomonads, a taxon correlated with disease suppression, and of two well-described antifungal genes (phlD coding for 2,4-DAPG and hcnAB coding for HCN). 3. We show an enrichment of fluorescent pseudomonads, phlD and hcnAB, upon pathogen infection. This effect was only measurable in the uninfected root compartment. We link these effects to an increased chemotaxis of pseudomonads towards exudates of infected plants. 4. Synthesis. We conclude that barley plants selectively recruited bacteria carrying antifungal traits upon pathogen attack and that the pathogen application locally interfered with this process. By disentangling these two effects, we set the base for enhancing strategies unravelling how pathogens and plant hosts jointly shape microbiome functionality

Effects of Lip Revision Surgery in Cleft Lip/Palate Patients

The decision for lip revision surgery in patients with repaired cleft lip/palate is based on surgeons’ subjective evaluation of lip disability. An objective evaluation would be highly beneficial for the assessment of surgical outcomes. In this study, the effects of lip revision on circumoral movements were objectively quantified. The hypothesis was that lip revision increases scarring and impairment. The study was a non-randomized clinical trial that included patients with cleft lip who had revision, patients with cleft lip who did not, and non-cleft control individuals. Three-dimensional facial movements were measured. Revision patients were measured before and after surgery. Other individuals were measured at similar intervals. Regression models were fit to summary measurements, and changes were modeled. Patients with repaired cleft lip/palate had fewer mean movements than control individuals. Lip revision did not worsen mean movements; however, individual patients’ movements varied from ‘improvement’ to ‘no change’ to ‘worse’ relative to those of control individuals

Data from: Short-term climate change manipulation effects do not scale up to long-term legacies: effects of an absent snow cover on boreal forest plants

1. Despite time lags and non-linearity in ecological processes, the majority of our knowledge about ecosystem responses to long-term changes in climate originates from relatively short-term experiments. 2. We utilized the longest ongoing snow removal experiment in the world and an additional set of new plots at the same location in northern Sweden to simultaneously measure the effects of long-term (11 winters) and short-term (1 winter) absence of snow cover on boreal forest understorey plants, including effects on root growth and phenology. 3. Short-term absence of snow reduced vascular plant cover in the understorey by 42%, reduced fine root biomass by 16%, reduced shoot growth by up to 53%, and induced tissue damage on two common dwarf shrubs. In the long-term manipulation, more substantial effects on understorey plant cover (92% reduced) and standing fine root biomass (39% reduced) were observed, whereas other response parameters, such as tissue damage, were observed less. Fine root growth was generally reduced, and its initiation delayed by c. 3 (short-term) to 6 weeks (long-term manipulation). 4. Synthesis We show that one extreme winter with a reduced snow cover can already induce ecologically significant alterations. We also show that long-term changes were smaller than suggested by an extrapolation of short-term manipulation results (using a constant proportional decline). In addition, some of those negative responses, such as frost damage and shoot growth, were even absolutely stronger in the short-term compared to the long-term manipulation. This suggests adaptation or survival of only those individuals that are able to cope with these extreme winter conditions, and that the short-term manipulation alone would over-predict long-term impacts. These results highlight both the ecological importance of snow cover in this boreal forest, and the value of combining short- and long-term experiments side by side in climate change research

Power analysis for generalized linear mixed models in ecology and evolution

‘Will my study answer my research question?’ is the most fundamental question a researcher can ask when designing a study, yet when phrased in statistical terms – ‘What is the power of my study?’ or ‘How precise will my parameter estimate be?’ – few researchers in ecology and evolution (EE) try to answer it, despite the detrimental consequences of performing under- or over-powered research. We suggest that this reluctance is due in large part to the unsuitability of simple methods of power analysis (broadly defined as any attempt to quantify prospectively the ‘informativeness’ of a study) for the complex models commonly used in EE research. With the aim of encouraging the use of power analysis, we present simulation from generalized linear mixed models (GLMMs) as a flexible and accessible approach to power analysis that can account for random effects, overdispersion and diverse response distributions.<p></p> We illustrate the benefits of simulation-based power analysis in two research scenarios: estimating the precision of a survey to estimate tick burdens on grouse chicks and estimating the power of a trial to compare the efficacy of insecticide-treated nets in malaria mosquito control. We provide a freely available R function, sim.glmm, for simulating from GLMMs.<p></p> Analysis of simulated data revealed that the effects of accounting for realistic levels of random effects and overdispersion on power and precision estimates were substantial, with correspondingly severe implications for study design in the form of up to fivefold increases in sampling effort. We also show the utility of simulations for identifying scenarios where GLMM-fitting methods can perform poorly.<p></p> These results illustrate the inadequacy of standard analytical power analysis methods and the flexibility of simulation-based power analysis for GLMMs. The wider use of these methods should contribute to improving the quality of study design in EE.<p></p&gt