Molluscs Community as a Keystone Group for Assessing the Impact of Urban Sprawl at Intertidal Ecosystems

Mollusc communities are getting endangered as a result of urban sprawl because artificial structures do not surrogate natural substrates. In this study, we compared diversity and community and trophic structures of molluscs among different models of artificial substrate and its adjacent natural rock, to detect relationships between some abiotic variables and the molluscs community. Complexity, chemical composition and age were tested as potential drivers of the community. Diversity, community and trophic structure differed between natural and artificial substrates. Complexity at the scale of cm was detected as the most important factor driving community structure. Besides, a chemical composition based on silica and/or scarce calcium carbonates seems to be relevant for molluscs, as well as for the secondary substrate where they inhabit. However, age did not seem to be a driving factor. Among the different artificial structures, macroscale complexity was detected as the main factor diverging a drastically poor community at seawalls from other artificial structures.

Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current ordinary differential equation-based models fail to capture complex behaviors that fall outside of a predetermined ecological theory and do not scale well with increasing community complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of deep learning, the LSTM learns a high dimensional data-driven non-linear dynamical system model used to design communities with desired metabolite profiles. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model. We build methods decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to identify communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

Fitness for Purpose in Online Communities: Community Complexity Framework for Diagnosis and Design of Socio-Technical Systems

Recent discourse on Information and Communication Technologies’ (ICT) impact on societies has been dominated by negative side-effects of information exchange in huge online social systems. Yet, the size of ICT-based communities also provides an unprecedented opportunity for collective action, as exemplified through crowdfunding, crowdsourcing, or peer production. This paper aims to provide a framework for understanding what makes online collectives succeed or fail in achieving complex goals. The paper combines social and complexity sciences’ insights on structures, mechanics, and emergent phenomena in social systems to define a Community Complexity Framework for evaluating three crucial components of complexity: multi-level structuration, procedural self-organization, and common identity. The potential value of such a framework would be to shift the focus of efforts aimed at curing the malfunctions of online social systems away from the design of algorithms that can automatically solve such problems, and toward the development of technologies which enable online social systems to self-organize in a more productive and sustainable way.

Effects of maize (Zea mays) genotypes and microbial sources in shaping fall armyworm (Spodoptera frugiperda) gut bacterial communities

Plants can have fundamental roles in shaping bacterial communities associated with insect herbivores. For larval lepidopterans (caterpillars), diet has been shown to be a driving force shaping gut microbial communities, where the gut microbiome of insects feeding on different plant species and genotypes can vary in composition and diversity. In this study, we aimed to better understand the roles of plant genotypes, sources of microbiota, and the host gut environment in structuring bacterial communities. We used multiple maize genotypes and fall armyworm (Spodoptera frugiperda) larvae as models to parse these drivers. We performed a series of experiments using axenic larvae that received a mixed microbial community prepared from frass from larvae that consumed field-grown maize. The new larval recipients were then provided different maize genotypes that were gamma-irradiated to minimize bacteria coming from the plant during feeding. For field-collected maize, there were no differences in community structure, but we did observe differences in gut community membership. In the controlled experiment, the microbial inoculation source, plant genotype, and their interactions impacted the membership and structure of gut bacterial communities. Compared to axenic larvae, fall armyworm larvae that received frass inoculum experienced reduced growth. Our results document the role of microbial sources and plant genotypes in contributing to variation in gut bacterial communities in herbivorous larvae. While more research is needed to shed light on the mechanisms driving this variation, these results provide a method for incorporating greater gut bacterial community complexity into laboratory-reared larvae.

Structural and Functional Variations of the Macrobenthic Community of the Adige Basin along the River Continuum

Since the publication of the River Continuum Concept (RCC), the capacity of the longitudinal dimension to predict the distribution of species and ecological functions in river networks was discussed by different river theories. The taxonomic structures and functional attributes of macrobenthic communities were investigated along the river continuum in the river Adige network (Northern Italy), with the aim to test the reliability of RCC theory and clarify the relation between structural and functional features in lotic systems. Distance from the spring was found to be most representative proxy among environmental parameters. The analysis highlighted the decrease of biodiversity levels along the river continuum. The decrease of taxonomic diversity corresponded to the loss in functional richness. The abundances of predator and walker taxa, as well as semelparous organisms, declined along the longitudinal gradient, suggesting variations in community complexity and granulometry. Regression models also depicted the presence of disturbed communities in the central section of the basin, where intensive agricultural activities occur, that affected environmental gradients. Overall, results offered evidences that the river continuum may predict macrobenthic community structures in terms of taxonomic diversity, thus confirming the general validity of RCC. Nonetheless, the functional analysis did not provide equally clear evidences to support the theory. After four decades from its postulation, the RCC is still a reliable model to predict the general macroinvertebrates distribution. However, community functions may respond to a number of local factors not considered in RCC, which could find a declination in other theories. The relations between structural and functional features confirmed to be complex and sensitive to disturbances and local conditions.

Monoassociation with bacterial isolates reveals the role of colonization, community complexity and abundance on locomotor behavior in larval zebrafish

Background Across taxa, animals with depleted intestinal microbiomes show disrupted behavioral phenotypes. Axenic (i.e., microbe-free) mice, zebrafish, and fruit flies exhibit increased locomotor behavior, or hyperactivity. The mechanism through which bacteria interact with host cells to trigger normal neurobehavioral development in larval zebrafish is not well understood. Here, we monoassociated zebrafish with either one of six different zebrafish-associated bacteria, mixtures of these host-associates, or with an environmental bacterial isolate. Results As predicted, the axenic cohort was hyperactive. Monoassociation with three different host-associated bacterial species, as well as with the mixtures, resulted in control-like locomotor behavior. Monoassociation with one host-associate and the environmental isolate resulted in the hyperactive phenotype characteristic of axenic larvae, while monoassociation with two other host-associated bacteria partially blocked this phenotype. Furthermore, we found an inverse relationship between the total concentration of bacteria per larvae and locomotor behavior. Lastly, in the axenic and associated cohorts, but not in the larvae with complex communities, we detected unexpected bacteria, some of which may be present as facultative predators. Conclusions These data support a growing body of evidence that individual species of bacteria can have different effects on host behavior, potentially related to their success at intestinal colonization. Specific to the zebrafish model, our results suggest that differences in the composition of microbes in fish facilities could affect the results of behavioral assays within pharmacological and toxicological studies.

Monitoring and Surveillance of Aerial Mycobiota of Rice Paddy through DNA Metabarcoding and qPCR

The airborne mycobiota has been understudied in comparison with the mycobiota present in other agricultural environments. Traditional, culture-based methods allow the study of a small fraction of the organisms present in the atmosphere, thus missing important information. In this study, the aerial mycobiota in a rice paddy has been examined during the cropping season (from June to September 2016) using qPCRs for two important rice pathogens (Pyricularia oryzae and Bipolaris oryzae) and by using DNA metabarcoding of the fungal ITS region. The metabarcoding results demonstrated a higher alpha diversity (Shannon–Wiener diversity index H′ and total number of observed species) at the beginning of the trial (June), suggesting a higher level of community complexity, compared with the end of the season. The main taxa identified by HTS analysis showed a shift in their relative abundance that drove the cluster separation as a function of time and temperature. The most abundant OTUs corresponded to genera such as Cladosporium, Alternaria, Myrothecium, or Pyricularia. Changes in the mycobiota composition were clearly dependent on the average air temperature with a potential impact on disease development in rice. In parallel, oligotyping analysis was performed to obtain a sub-OTU identification which revealed the presence of several oligotypes of Pyricularia and Bipolaris with relative abundance changing during monitoring.

Environment dependence of rhizobial relative fitness in the legume-rhizobia symbiosis

Spatial and temporal variation in resource availability, population density, and composition likely affect the ecology and evolution of symbiotic interactions. We examined how host genotype, Nitrogen addition, rhizobial density, and community complexity affected a legume-rhizobia (Medicago truncatula - Ensifer meliloti) mutualism. Host genotype had the strongest effect on the size, number, and rhizobial composition of root nodules (the symbiotic organ). By contrast, the effect of small changes in N-availability and the complexity of the inoculum community (2, 3, 8, or 68 strains) were minor. Higher inoculum density resulted in a nodule community that was less diverse and more beneficial but only in the more selective host. With the less selective host, higher density resulted in more diverse and less beneficial nodule communities. Density effects on strain composition deserve additional scrutiny as they can create eco-evolutionary feedback and have translational relevance for overcoming establishment barriers in bio-inoculants.Short AbstractThe environmental context of the nitrogen-fixing mutualism between leguminous plants and rhizobial bacteria varies over space and time. The understudied environmental variable of rhizobial density had a larger effect on the relative fitness of 68 rhizobia (Ensifer meliloti) strains in nodules than the addition of low-levels of nitrogen or community complexity.

Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units

Background Microorganisms are not only indispensable to ecosystem functioning, they are also keystones for emerging technologies. In the last 15 years, the number of studies on environmental microbial communities has increased exponentially due to advances in sequencing technologies, but the large amount of data generated remains difficult to analyze and interpret. Recently, metabarcoding analysis has shifted from clustering reads using Operational Taxonomical Units (OTUs) to Amplicon Sequence Variants (ASVs). Differences between these methods can seriously affect the biological interpretation of metabarcoding data, especially in ecosystems with high microbial diversity, as the methods are benchmarked based on low diversity datasets. Results In this work we have thoroughly examined the differences in community diversity, structure, and complexity between the OTU and ASV methods. We have examined culture-based mock and simulated datasets as well as soil- and plant-associated bacterial and fungal environmental communities. Four key findings were revealed. First, analysis of microbial datasets at family level guaranteed both consistency and adequate coverage when using either method. Second, the performance of both methods used are related to community diversity and sample sequencing depth. Third, differences in the method used affected sample diversity and number of detected differentially abundant families upon treatment; this may lead researchers to draw different biological conclusions. Fourth, the observed differences can mostly be attributed to low abundant (relative abundance < 0.1%) families, thus extra care is recommended when studying rare species using metabarcoding. The ASV method used outperformed the adopted OTU method concerning community diversity, especially for fungus-related sequences, but only when the sequencing depth was sufficient to capture the community complexity. Conclusions Investigation of metabarcoding data should be done with care. Correct biological interpretation depends on several factors, including in-depth sequencing of the samples, choice of the most appropriate filtering strategy for the specific research goal, and use of family level for data clustering.