Guided by Microbes: Applying Community Coalescence Principles for Predictive Microbiome Engineering

Every seed germinating in soils, wastewater treatment, and stream confluence exemplify microbial community coalescence—the blending of previously isolated communities. Here, we present theoretical and experimental knowledge on how separated microbial communities mix, with particular focus on managed ecosystems.

Are the interaction effects of warming and drought on nutritional status and biomass production in a tropical forage legume greater than their individual effects?

Climate change effects on natural and managed ecosystems are difficult to predict due to its multi-factor nature. However, most studies which investigate the impacts of climate change factors on plants, such as warming or drought, were conducted under one single stress and controlled environments. In this study, we evaluated the effects of elevated temperature (+ 2°C) (T) under different conditions of soil water availability (W) to understand the interactive effects of both factors on leaf, stem, and inflorescence macro and micronutrients concentration and biomass allocation of a tropical forage species, Stylosanthes capitata Vogel under field conditions. Temperature control was performed by a Temperature Free-Air Controlled Enhancement (T‐FACE) system. We observed that warming changed nutrient concentrations and plant growth depending on soil moisture levels, but the responses were specific for each plant-organ. In general, we observed that warming under well-watered conditions greatly improved nutrient concentration and biomass production, whilst the opposite effect was observed under non-irrigated and non-warmed conditions. However, under warmed and non-irrigated conditions, we observed that leaf biomass and leaf nutrient concentration greatly reduced when compared to non-warmed and irrigated plants. Our findings suggest that warming (2°C above ambient temperature) and drought, as well as both combined stresses, will change the nutrient requirements and biomass distributions between plant aerial organs of S. capitata in tropical ecosystems, which may impact animal feeding in the future.

Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery

The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.

Plant–Microbe Interactions: From Genes to Ecosystems Using Populus as a Model System

Plant–microbe symbioses span a continuum from pathogenic to mutualistic, with functional consequences for both organisms in the symbiosis. In order to increase sustainable food and fuel production in the future, it is imperative that we harness these symbioses. The tree genus Populus is an excellent model system for studies examining plant–microbe interactions due to the wealth of genomic information available and the molecular tools that have been developed to manipulate Populus–microbe symbioses. In this review, we highlight how Populus can serve as a model system to explore plant–microbe interactions. Specifically, we highlight research linking Populus–microbe interactions from the gene to the ecosystem level. We explore why Populus is an excellent model for perennial plant systems, the molecular underpinnings of Populus–microbe interactions, how host genetics influence microbial community composition, and how microbial communities vary at fine spatial scales and between Populus spp. Furthermore, we explore how changes in the microbiome may affect ecosystem-level functions in managed and natural ecosystems. Understanding and manipulating these interactions in Populus has the potential to improve plant health and affect ecosystem sustainability and processes because Populus trees function as foundational species in many natural ecosystems and are also deployed in managed ecosystems for various agroforestry applications. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Pollinators mediate floral microbial diversity and network under agrochemical disturbance

How pollinators mediate microbiome assembly in the anthosphere is a major unresolved question of theoretical and applied importance in the face of anthropogenic disturbance. We addressed this question by linking visitation of diverse pollinator functional groups (bees, wasps, flies, butterflies, beetles, true bugs and other taxa) to the key properties of floral microbiome (microbial α- and β-diversity and microbial network) under agrochemical disturbance, using a field experiment of bactericide and fungicide treatments on cultivated strawberries that differ in flower abundance. Structural equation modeling was used to link agrochemical disturbance and flower abundance to pollinator visitation to floral microbiome properties. Our results revealed that (1) pollinator visitation influenced the α- and β-diversity and network centrality of floral microbiome, with different pollinator functional groups affecting different microbiome properties; (2) flower abundance influenced floral microbiome both directly by governing the source pool of microbes and indirectly by enhancing pollinator visitation; and (3) agrochemical disturbance affected floral microbiome primarily directly by fungicide, and less so indirectly via pollinator visitation. These findings improve the mechanistic understanding of floral microbiome assembly, and may be generalizable to many other plants that are visited by diverse insect pollinators in natural and managed ecosystems.