Ocean currents shape the microbiome of Arctic marine sediments

Prokaryote communities were investigated on the seasonally stratified Alaska Beaufort Shelf (ABS). Water and sediment directly underlying water with origin in the Arctic, Pacific or Atlantic oceans were analyzed by pyrosequencing and length heterogeneity-PCR in conjunction with physicochemical and geographic distance data to determine what features structure ABS microbiomes. Distinct bacterial communities were evident in all water masses. Alphaproteobacteria explained similarity in Arctic surface water and Pacific derived water. Deltaproteobacteria were abundant in Atlantic origin water and drove similarity among samples. Most archaeal sequences in water were related to unclassified marine Euryarchaeota. Sediment communities influenced by Pacific and Atlantic water were distinct from each other and pelagic communities. Firmicutes and Chloroflexi were abundant in sediment, although their distribution varied in Atlantic and Pacific influenced sites. Thermoprotei dominated archaea in Pacific influenced sediments and Methanomicrobia dominated in methane-containing Atlantic influenced sediments. Length heterogeneity-PCR data from this study were analyzed with data from methane-containing sediments in other regions. Pacific influenced ABS sediments clustered with Pacific sites from New Zealand and Chilean coastal margins. Atlantic influenced ABS sediments formed another distinct cluster. Density and salinity were significant structuring features on pelagic communities. Porosity co-varied with benthic community structure across sites and methane did not. This study indicates that the origin of water overlying sediments shapes benthic communities locally and globally and that hydrography exerts greater influence on microbial community structure than the availability of methane.     

Experimental temperatures shape host microbiome diversity and composition

Global climate change has led to more extreme thermal events. Plants and animals harbour diverse microbial communities, which may be vital for their physiological performance and help them survive stressful climatic conditions. The extent to which microbiome communities change in response to warming or cooling may be important for predicting host performance under global change. Using a meta-analysis of 1377 microbiomes from 43 terrestrial and aquatic species, we found a decrease in the amplicon sequence variant-level microbiome phylogenetic diversity and alteration of microbiome composition under both experimental warming and cooling. Microbiome beta dispersion was not affected by temperature changes. We showed that the host habitat and experimental factors affected microbiome diversity and composition more than host biological traits. In particular, aquatic organisms—especially in marine habitats—experienced a greater depletion in microbiome diversity under cold conditions, compared to terrestrial hosts. Exposure involving a sudden long and static temperature shift was associated with microbiome diversity loss, but this reduction was attenuated by prior-experimental lab acclimation or when a ramped regime (i.e., warming) was used. Microbial differential abundance and co-occurrence network analyses revealed several potential indicator bacterial classes for hosts in heated environments and on different biome levels. Overall, our findings improve our understanding on the impact of global temperature changes on animal and plant microbiome structures across a diverse range of habitats. The next step is to link these changes to measures of host fitness, as well as microbial community functions, to determine whether microbiomes can buffer some species against a more thermally variable and extreme world.     

Host genotype controls ecological change in the leaf fungal microbiome

Leaf fungal microbiomes can be fundamental drivers of host plant success, as they contain pathogens that devastate crop plants and taxa that enhance nutrient uptake, discourage herbivory, and antagonize pathogens. We measured leaf fungal diversity with amplicon sequencing across an entire growing season in a diversity panel of switchgrass (Panicum virgatum). We also sampled a replicated subset of genotypes across 3 additional sites to compare the importance of time, space, ecology, and genetics. We found a strong successional pattern in the microbiome shaped both by host genetics and environmental factors. Further, we used genome-wide association (GWA) mapping and RNA sequencing to show that 3 cysteine-rich receptor-like kinases (crRLKs) were linked to a genetic locus associated with microbiome structure. We confirmed GWAS results in an independent set of genotypes for both the internal transcribed spacer (ITS) and large subunit (LSU) ribosomal DNA markers. Fungal pathogens were central to microbial covariance networks, and genotypes susceptible to pathogens differed in their expression of the 3 crRLKs, suggesting that host immune genes are a principal means of controlling the entire leaf microbiome.

Leaf fungal microbiomes can strongly influence host plant success. Monitoring the leaf fungal microbiome of switchgrass over time shows microbial ecological succession, and reveals the host plant genes that influence community-wide changes.     

雄安新区多尺度生态基础设施规划

生态基础设施是保持、改善和增加生态系统服务的条件和过程,对于提升区域生态系统服务能力具有重要意义。已有的生态基础设施规划方法主要针对单一尺度进行,不能体现生态系统服务在不同尺度间的相互影响。基于ArcGIS、最小累积阻力（MCR）模型和层次分析法（AHP）构建了一种"宏观-中观-微观"多尺度生态基础设施核心区识别和生态廊道辨识的方法体系框架。以雄安新区为例,提出生态基础设施规划方案：宏观尺度需维持气候调节、固碳释氧、保护生物多样性、防风固沙等功能的稳定,生态核心区斑块面积建议大于10 km²,生态廊道宽度设置为100-200 m;中观尺度生态核心区斑块面积不小于5 km²,宽度设置为50-100 m,可改善区域水源涵养、文化休闲、净化环境、减弱噪声等生态系统服务功能;微观尺度生态核心区斑块需大于1 km²,宽度为10-30 m,可有效控制径流、净水调蓄。得出以下结论：（1）不同尺度生态基础设施规划目标不同,规模有所差别,其中宏观尺度规模最大,微观最小;（2）多尺度规划方法,可以科学有效的指导生态基础设施建设,提高区域生态系统服务能力;（3）多尺度生态基础设施规划建设需要向多学科理论和方法的交叉融合方向发展。研究结论可为今后全面推进雄安新区的可持续发展提供科学基础和决策参考。     

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.     

A method to the madness: Disentangling the individual forces that shape the rumen microbiome

The rumen microbiome ‐ a remarkable example of obligatory symbiosis with high ecological and social relevance Subject Categories: Digestive System, Ecology, Microbiology, Virology & Host Pathogen Interaction

Ruminants are intimately linked to mankind since their domestication some 8,000 years ago, and their close relationship may have well been one of the main drivers of human civilization (Diamond, 1997). Ruminants—cattle, sheep, goats, deer, gazelles, and so on—also embody the close link between solar energy transformed via photosynthesis and digestion into consumable products, such as meat, milk, leather, or wool, that have sustained humanity for millennia. Throughout this shared history, constant improvements through breeding, husbandry, and industrial livestock farming have greatly increased the production of milk, meat, and other animal‐based products.Ruminants, more so than any other mammalian group also represent the epitome of mammalian‐microbe symbiosis, as they rely completely on microbial fermentation to sustain their lives. In the rumen, the fermentative organ situated in the upper gastrointestinal tract resides a vast microbial community from all domains of life—bacteria, archaea, and eukarya—that turn indigestible plant feed into food for the animal. The rumen microbiome produces up to 70% of the energy the animal needs for growth and maintenance, and, from mankind''s perspective, for the production of food and other consumables.

Ruminants, more so than any other mammalian group, also represent the epitome of mammalian‐microbe symbiosis, as they rely completely on microbial fermentation to sustain their lives.

With growing understanding that these microorganisms are responsible for degrading plant material and supplying nutrients for the animals, a new research discipline emerged along with aspirations to improve the yield of livestock farming. While most research had understandably focused on production efficiency, it also showed that the rumen microbiome is intricately linked to many other phenotypes of the animal. This understanding comes at a time when we increasingly realize that mankind''s actions have a detrimental effect on the environment. The microbial fermentation in the rumen produces large amounts of methane, a potent greenhouse gas that has been demonstrated to contribute to global climate change. We therefore need to consider both our increased demand for meat and milk products and aim to mitigate the negative environmental impact of intensive livestock farming. Modulating the microbial community to sustain or further increase productivity while decreasing methane emissions has indeed become a major goal for microbial ecologists studying the rumen microbiome and its interactions with the host animal. In this article, we discuss the driving forces that affect the establishment and composition of the rumen microbiome and its plasticity, and potential avenues for harnessing these forces for a more sustainable production of animal products.     

Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism

1. Download : Download high-res image (231KB)
2. Download : Download full-size image

     

Multi-scale trajectory analysis: powerful conceptual tool for understanding ecological change

The model at the basis of trajectory analysis is conceptually simple. When applied to time series vegetation data, the projectile becomes a surrogate for vegetation state, the trajectory for the evolving vegetation process, and the properties of the trajectory for the true process characteristics. Notwithstanding its simplicity, the model is well-defined under natural circumstances and easily adapted to serial vegetation data, irrespective of source. As a major advantage, compared to other models that isolate the elementary processes and probe vegetation dynamics for informative regularities on the elementary level, the trajectory model allows us to probe for regularities on the level of the highest process integrity. Theories and a data analytical methodology developed around the trajectory model are outlined, including many numerical examples. A rich list of key references and volumes of supplementary information supplied in the Web Only Appendices rounds out the presentation.     

Multi-scale trajectory analysis:powerful conceptual tool for understanding ecological change

The model at the basis of trajectory analysis is conceptually simple.When applied to time series vegetation data,the projectile becomes a surrogate for vegetation state,the trajectory for the evolving vegetation process,and the properties of the trajectory for the true process characteristics.Notwithstanding its simplicity,the model is well-defined under natural circumstances and easily adapted to serial vegetation data,irrespective of source.As a major advantage,compared to other models that isolate the elementary processes and probe vegetation dynamics for informative regularities on the elementary level,the trajectory model allows us to probe for regularities on the level of the highest process integrity.Theories and a data analytical methodology developed around the trajectory model are outlined,including many numerical examples.A rich list of key references and volumes of supplementary information supplied in the Web Only Appendices rounds out the presentation.     

Application of ecological and evolutionary theory to microbiome community dynamics across systems

A fundamental aim of microbiome research is to understand the factors that influence the assembly and stability of host-associated microbiomes, and their impact on host phenotype, ecology and evolution. However, ecological and evolutionary theories applied to predict microbiome community dynamics are largely based on macroorganisms and lack microbiome-centric hypotheses that account for unique features of the microbiome. This special feature sets out to drive advancements in the application of eco-evolutionary theory to microbiome community dynamics through the development of microbiome-specific theoretical and conceptual frameworks across plant, human and non-human animal systems. The feature comprises 11 research and review articles that address: (i) the effects of the microbiome on host phenotype, ecology and evolution; (ii) the application and development of ecological and evolutionary theories to investigate microbiome assembly, diversity and stability across broad taxonomic scales; and (iii) general principles that underlie microbiome diversity and dynamics. This cross-disciplinary synthesis of theoretical, conceptual, methodological and analytical approaches to characterizing host–microbiome ecology and evolution across systems addresses key research gaps in the field of microbiome research and highlights future research priorities.     

黄土高原典型脆弱区生态安全多尺度评价

区域生态安全目前受到广泛重视,生态脆弱区的生态安全评价及驱动因子研究尤为重要.以黄土高原生态脆弱区为研究对象,从市域和县域两个尺度上,分析案例区的生态安全驱动指标,评价其生态安全状况.选择了榆林市和横山县两个行政单元,针对区域特征,构建了不同尺度上的生态安全指标体系,利用层次分析法,对不同尺度上生态安全的空间分异进行了研究.结果表明,榆林市12个县的生态安全水平存在一定程度的差异,基本上是东北低,西南高.横山县总体也是北部、东北部生态安全水平低,而西南部高生态安全水平在不同尺度上存在着差异.不同尺度上气候、地貌因子所占的权重不大,而人类干扰因子所占的权重较大.     

The microbiome associated with two Synechococcus ribotypes at different levels of ecological interaction

Planktonic cyanobacteria belonging to the genus Synechococcus are ubiquitously distributed in marine and fresh waters, substantially contributing to total carbon fixation on a global scale. While their ecological relevance is acknowledged, increasing resolution in molecular techniques allows disentangling cyanobacteria's role at the micro‐scale, where complex microbial interactions may drive the overall community assembly. The interplay between phylogenetically different Synechococcus clades and their associated bacterial communities can affect their ecological fate and susceptibility to protistan predation. In this study, we experimentally promoted different levels of ecological interaction by mixing two Synechococcus ribotypes (MW101C3 and LL) and their associated bacteria, with and without a nanoflagellate grazer (Poterioochromonas sp.) in laboratory cultures. The beta‐diversity of the Synechococcus‐associated microbiome in laboratory cultures indicated that the presence of the LL ribotype was the main factor determining community composition changes (41% of total variance), and prevailed over the effect of protistan predation (18% of total variance). Our outcomes also showed that species coexistence and predation may promote microbial diversity, thus highlighting the underrated ecological relevance of such micro‐scale factors.     

Intestinal hypoxia-inducible factor 2α regulates lactate levels to shape the gut microbiome and alter thermogenesis

1. Download : Download high-res image (140KB)
2. Download : Download full-size image

     

The shape of an ecological trade-off varies with environment

Central to most theories that explain the diversity of life is the concept that organisms face trade-offs. Theoretical work has shown that the precise shape of a trade-off relationship affects evolutionary predictions. One common trade-off is that between competitive ability and resistance to predators, parasitoids, pathogens or herbivores. We used a microbial experimental system to elucidate the shape of the relationship between parasitoid resistance and competitive ability. For each of 86 bacteriophage-resistant isolates of the bacterium Escherichia coli B, we measured the degree of resistance to bacteriophage T2 (a viral parasitoid) and relative competitive ability in both the resource environment in which strains were isolated and in two alternate environments. We observed that environmental change can alter trade-off shape, and that different physiological mechanisms can lead to different trade-off shapes and different sensitivities to environmental change. These results highlight the important interaction between environment and trade-off shape in affecting ecological and evolutionary dynamics.