The unseen effect of pesticides: The impact on phytobiota structure and functions

In the last years, the diffusion and implementation of next-generation sequencing and the reduction of costs raised the interest in phytyobiome studies allowing to dissect the ecological interactions regulating the holobiont. Indeed, crop plants are associated with a wide diversity of microorganisms in all their parts. Crop microbiota influences plant phenotype, growth, yield and quality by contributing to plant resistance toward diseases, plant adaptation to abiotic stresses, and plant nutrition. The association between terrestrial plants and microbes developed at least 460 million years ago, as suggested by the fossil evidence of the earliest land plants, indicating the essential role of microbes for plants. Recent studies indicate that plants actively recruit beneficial microorganisms to facilitate their adaptation to environmental conditions. Cultivation methods and disease control measures can influence plant microbiome structure and functions. Both pesticide and biological control agent applications may alter the biodiversity inside the phytobiota and suppress beneficial functions. Nonetheless, to date, the effects of disease control measures on phytobiota and their possible side consequences on plant growth, crop productivity and quality remain a neglected field of study. The present work summarizes the known effects on phytobiota providing evidence about the role of plant microbial community in determining the overall efficacy of the applied control measure and suggests that future studies on plant disease control consider also the microbe-mediated effects on plant fitness

Effects of landing gear, speed brake and protuberances on the longitudinal aerodynamic characteristics of an NASA supercritical-wing research airplane model

An investigation was conducted in the Langley Research Center 8-foot transonic pressure tunnel to determine the effects of the landing gear, speed brake and the major airplane protuberances on the longitudinal aerodynamic characteristics of an 0.087-scale model of the TF-8A supercritical-wing research airplane. For the effects of the landing gear and speed brake, tests were conducted at Mach numbers of 0.25 and 0.35 with a flap deflection of 20 degrees and a horizontal-tail angle of -10 degrees. These conditions simulated those required for take-off and landing. The effects of the protuberances were determined with the model configured for cruise (i.e., horizontal-tail angle of -2.5 degrees and no other control deflection), and these tests were conducted at Mach numbers from 0.50 to 1.00. The angle-of-attack range for all tests varied from about -5 degrees to 12 degrees

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

Preconditioning effects of intermittent stream flow on leaf litter decomposition

Autumnal input of leaf litter is a pivotal energy source in most headwater streams. In temporary streams, however, water stress may lead to a seasonal shift in leaf abscission. Leaves accumulate at the surface of the dry streambed or in residual pools and are subject to physicochemical preconditioning before decomposition starts after flow recovery. In this study, we experimentally tested the effect of photodegradation on sunlit streambeds and anaerobic fermentation in anoxic pools on leaf decomposition during the subsequent flowing phase. To mimic field preconditioning, we exposed Populus tremula leaves to UV-VIS irradiation and wet-anoxic conditions in the laboratory. Subsequently, we quantified leaf mass loss of preconditioned leaves and the associated decomposer community in five low-order temporary streams using coarse and fine mesh litter bags. On average, mass loss after approximately 45 days was 4 and 7% lower when leaves were preconditioned by irradiation and anoxic conditions, respectively. We found a lower chemical quality and lower ergosterol content (a proxy for living fungal biomass) in leaves from the anoxic preconditioning, but no effects on macroinvertebrate assemblages were detected for any preconditioning treatment. Overall, results from this study suggest a reduced processing efficiency of organic matter in temporary streams due to preconditioning during intermittence of flow leading to reduced substrate quality and repressed decomposer activity. These preconditioning effects may become more relevant in the future given the expected worldwide increase in the geographical extent of intermittent flow as a consequence of global change. © 2011 Springer Basel AG

Does organic farming increase raspberry quality, aroma and beneficial bacterial biodiversity?

Plant-associated microbes can shape plant phenotype, performance, and productivity. Cultivation methods can influence the plant microbiome structure and differences observed in the nutritional quality of differently grown fruits might be due to variations in the microbiome taxonomic and functional composition. Here, the influence of organic and integrated pest management (IPM) cultivation on quality, aroma and microbiome of raspberry (Rubus idaeus L.) fruits was evaluated. Differences in the fruit microbiome of organic and IPM raspberry were examined by next-generation sequencing and bacterial isolates characterization to highlight the potential contribution of the resident-microflora to fruit characteristics and aroma. The cultivation method strongly influenced fruit nutraceutical traits, aroma and epiphytic bacterial biocoenosis. Organic cultivation resulted in smaller fruits with a higher anthocyanidins content and lower titratable acidity content in comparison to IPM berries. Management practices also influenced the amounts of acids, ketones, aldehydes and monoterpenes, emitted by fruits. Our results suggest that the effects on fruit quality could be related to differences in the population of Gluconobacter, Sphingomonas, Rosenbergiella, Brevibacillus and Methylobacterium on fruit. Finally, changes in fruit aroma can be partly explained by volatile organic compounds (VOCs) emitted by key bacterial genera characterizing organic and IPM raspberry fruit