Hasil untuk "Microbial ecology"

Aleksej Zelezniak, S. Andrejev, Olga Ponomarova et al.

V. Torsvik, L. Øvreås

J. Bever, I. Dickie, E. Facelli et al.

J. Vestal, D. White

N. Lee, P. Nielsen, K. Andreasen et al.

C. Vacher, A. Hampe, A. Porté et al.

Lucia Žifčáková, T. Větrovský, A. Howe et al.

Manon Gilson, Guillaume Bayon-Vicente, Simone Krings et al.

ABSTRACT Purple non-sulfur bacteria (PNSB) are well known to have an exceptional metabolic versatility. However, while the growth of PNSB on sugar-rich streams has been extensively explored, their ability to metabolize sugars is poorly understood. Here, we explore the metabolic mechanisms of sucrose, glucose, and fructose utilization in two phototrophic PNSB, Rhodospirillum rubrum and Rhodobacter capsulatus. Our findings demonstrate distinct carbohydrate assimilation capacities, as well as the use of different metabolic strategies for each species. Moreover, a trophic link was identified between the two species during co-cultivation, resulting from the production of fermentation by-products by Rh. capsulatus, which are then reassimilated by Rs. rubrum. Finally, we demonstrate that the synergy observed between Rs. rubrum and Rh. capsulatus can be successfully scaled up in a photobioreactor system. Our study highlights how fundamental knowledge of metabolism and the establishment of a trophic link between two PNSB species might be useful for the development of biobased economy and resource recovery strategies.IMPORTANCEThe diverse metabolic capacities found in microbial communities expand the possibilities of microbial biotechnological exploitation. In this study, we demonstrate that Rhodospirillum rubrum and Rhodobacter capsulatus, two purple non-sulfur bacteria, adopt different metabolic strategies for sugar assimilation. These differences allow them to benefit from each other, resulting in enhanced carbon yield and productivity compared to pure cultures. We also showed that the trophic link between both species can be scaled up in a photobioreactor system. Understanding these interactions expands the potential for designing microbial consortia optimized for the valorization of carbohydrate-rich waste streams using purple non-sulfur bacteria.

Die Tang, Shuangshuang Chen, Chuang Tang et al.

This study systematically characterized functional compartmentalization along the intestinal tract of New Zealand rabbits by analyzing mucosal tissue and luminal contents from distinct segments, including the duodenum, jejunum, ileum, cecum, and colon, using RNA-seq and 16S rRNA sequencing. Transcriptomic analysis revealed that differentially expressed genes identified between the small and large intestines were mainly enriched in digestion, absorption, and immune functions. Genes associated with the transport of amino acids, sugars, vitamins, and bile salts showed significantly higher expression in the small intestine, whereas genes related to water absorption, short-chain fatty acids (SCFAs), nucleotides, and metal ion transport were preferentially expressed in the large intestine. From an immunological perspective, genes involved in fungal responses were enriched in the small intestine, while bacterial response pathways and pattern recognition receptor (PRR) signaling genes were upregulated in the large intestine. Microbiota analysis demonstrated significantly greater diversity and abundance in the large intestine compared with the small intestine. Specifically, Proteobacteria and Actinobacteria were enriched in the small intestine, whereas Firmicutes, Verrucomicrobia, and Bacteroidetes dominated the large intestine. Correlation analysis further identified significant associations between gut microbiota composition and host genes involved in nutrient digestion and absorption. Together, these findings provide transcriptome-based evidence for regional specialization of nutrient transport, immune responses, and microbial ecology along the rabbit intestine.

Ni Kadek Dita Cahyani, Aji Wahyu Anggoro, Rian Prasetia et al.

ABSTRACT Marine Protected Areas (MPAs) play a crucial role in conserving marine biodiversity while providing ecological, social, and economic benefits. Effective monitoring is essential for assessing changes in biodiversity and ensuring the sustainability of MPAs. In this context, biodiversity monitoring in Karimunjawa National Park (KNP) provides an excellent opportunity to examine effective monitoring practices. Traditionally, biodiversity assessments have been conducted through visual census methods, which have limitations such as challenges in species identification, time constraints, and high survey costs. To complement visual surveys, this study employed environmental DNA (eDNA) metabarcoding with the 12S rRNA gene, utilizing Oxford Nanopore sequencing to assess fish diversity across different zonation systems within KNP. eDNA analysis detected a total of 183 fish species, with 87 species (38% of the 229 species recorded by visual census) and 25 families (71%) shared between the two methods. Alpha diversity (ANOVA, p > 0.05) showed no significant differences between sites and zonation, whereas community structure (PERMANOVA, p < 0.05) revealed significant differences between sites and zonation. Additionally, eDNA offered complementary insights by detecting broader functional traits than the visual census, such as nocturnal behavior, habitat preferences, and migratory variations of fish species, whereas the visual census predominantly only recorded reef‐associated and nonmigratory taxa. These findings demonstrate that eDNA, particularly when integrated with Oxford Nanopore sequencing, is a powerful tool for marine biodiversity monitoring. Standardizing bioinformatics workflows is crucial for ensuring data comparability and maximizing the effectiveness of eDNA‐based conservation strategies in Indonesia's MPAs.

Alberto Dinelli, Ada Altieri, Julien Tailleur

The self-organization of microbial ecosystems involves a large variety of mechanisms, ranging from biochemical signaling to population dynamics. Among these, the role of motility regulation has been little studied, despite the importance of active migration processes. Here we show how weak, random motility regulation generically induces the fragmentation of bacterial ecosystems comprising a large number of coexisting strains. To do so, we simulate microscopic models of run-and-tumble bacteria whose self-propulsion speeds are regulated by the local density of each strain. Our simulations reveal that, as the heterogeneity of the interaction network increases, the ecosystem undergoes a phase transition leading to the emergence of distinct communities. To account for these results and assess their robustness, we use random-matrix theory to analyze the hydrodynamic description of the bacterial ecosystem, obtaining a quantitative agreement with our microscopic simulations. Our results are shown to hold for a variety of motility-regulation mechanisms and should be relevant to the study of community formation by motile organisms.

Laurinne J Balstad, Joe Brennan, Marissa L. Baskett et al.

Theoretical ecologists have long leveraged empirical data in various forms to advance ecology. Recently increased volumes and access to ecological data present an expanding set of opportunities for theoreticians to inform model development, framing, and interpretation. Whereas statisticians have collective guidance on best practices for data use, theoreticians might lack formal education on how to integrate diverse types of data into a single ecological model. As a group of predominantly early-career theoretical ecologists, we have developed guiding principles and practical tips to support theoretical ecologists in synthesizing multiple types of data at different phases of the modeling process. Our rules fall into three overarching themes: iteration in the data-model integration process, leveraging multiple sources of data), and understanding uncertainty. Across these rules, we emphasize that the data-model integration requires transparent, justifiable, and defensible communication of modeling choices to support readers in appropriately contextualizing the model and its implications.

S. Taugourdeau, S. Taugourdeau, M. Machdoud et al.

This position article advocates for the creation of a participatory pastoral observatory, leveraging accessible technologies, including smartphones, to monitor rangeland ecosystems. Rather than reiterating the already accepted need for monitoring, we focus on how technological progress, ranging from ground-based field plots to satellite imagery, UAVs, and smartphones using Structure from Motion (SfM) methods, has transformed rangeland observation. We argue that an imagery-based community monitoring system can provide accurate, relevant, and timely data while empowering local stakeholders and informing policy decisions. We detail the operational steps of smartphone-based observatory, highlight its capacity to reduce labor-intensive biomass sampling, and discuss its feasibility when applied by pastoralists. We also draw lessons from related participatory approaches in Mongolia, Ireland, and East Africa. By integrating traditional ecological knowledge with scientific approaches, this initiative can strengthen the resilience of pastoral systems, support sustainable management practices, and contribute to evidence-based policymaking. The proposed observation framework builds on existing research and technological innovations to promote a decentralized and inclusive monitoring system. We imagine such a type of observatory could be useful in Sahel Region or in Northern Africa, could describe practical challenges (smartphone penetration, network coverage, training for low-literacy users), and outline next steps for implementation.

Masayuki K. Sakata, Takashi Kanbe, Shunpei Sato et al.

ABSTRACT Environmental DNA (eDNA) analyses provide valuable ecological data. Recent studies have explored eDNA dynamics related to reproductive behavior and developmental stages, revealing significant variations in eDNA concentrations across different life stages. However, there is a gap in understanding the association between eDNA concentrations and changes before and after developmental events, such as egg hatching. This study addresses this gap by monitoring eDNA signals in chum salmon (Oncorhynchus keta) during their early developmental stages and examining the effects of their physiological and behavioral changes. For this purpose, eDNA flux was monitored in rearing experiments with chum salmon during their developmental stages (egg, alevin, and fry). The eDNA flux varied significantly across different developmental stages: while no eDNA was detected during the egg stage, eDNA flux increased rapidly after hatching. After hatching, the eDNA flux became stable during the alevin stage but increased approximately 30‐fold when they progressed into the fry stage (LMM and post hoc Tukey‐HSD test: p < 0.05). These results suggest that eDNA signals vary across the developmental stages and can be utilized to estimate and monitor fish development even under natural conditions, such as those occurring under gravel for salmonid species.

Karen L. Adair, A. Douglas

Shipeng Xu

The study of ecological networks is crucial for modern conservation biology, addressing habitat fragmentation and biodiversity loss, especially in complex regions. These networks, including corridors, sources, and nodes, are key for species movement and ecosystem functioning. The Periphery Analysis Model (PAM) is introduced as a new approach to study the periphery of these networks, focusing on peripheral nodes' role in environmental change response and network resilience. PAM, drawing from graph theory, complex network analysis, and landscape ecology, uses the Periphery Uniqueness Index (PuI) and the Periphery Balance Index (PbI) to measure peripheral nodes' attributes and balance. It also offers derived indices for a detailed understanding of the periphery's influence. By revealing the periphery's defining characteristics, PAM enhances knowledge of ecological networks' structural features, providing insights for biodiversity, connectivity, and ecosystem health. The research encourages integrating PAM into conservation strategies to inform policy for ecosystem preservation amid environmental challenges.

Aditya Mahadevan, Daniel S. Fisher

Feedbacks between evolution and ecology are ubiquitous, with ecological interactions determining which mutants are successful, and these mutants in turn modifying community structure. We study the evolutionary dynamics of several ecological models with overlapping niches, including consumer resource and Lotka-Volterra models. Evolution is assumed slow and extinctions are permanent, with ecological dynamics reaching a stable fixed point between introductions of invaders or mutants. When new strains are slowly added to the community, the ecosystem converges, after an initial evolutionary transient, to a diverse eco-evolutionary steady state. In this "Red Queen" phase of continual evolution, the biodiversity continues to turn over without the invasion probability of new variants getting any smaller. For resource-mediated interactions, the Red Queen phase obtains for any amount of asymmetry in the interactions between strains, and is robust to "general fitness" differences in the intrinsic growth rates of strains. Via a dynamical mean field theory framework valid for high-dimensional phenotype space, we analytically characterize the Red Queen eco-evolutionary steady state in a particular limit of model parameters. Scaling arguments enable a more general understanding of the steady state and evolutionary transients toward it. This work therefore establishes simple models of continual evolution in an ecological context without host-pathogen arms races, and points to the generality of Red Queen evolution. However, we also find other eco-evolutionary phases in simple models: For generalized Lotka-Volterra models with weakly asymmetric interactions an "oligarch" phase emerges in which the evolutionary dynamics continually slow down and a substantial fraction of the community's abundance condenses into a handful of slowly turning-over strains.

Jaye Sudweeks, Christoph Hauert

Phages use bacterial host resources to replicate, intrinsically linking phage and host survival. To understand phage dynamics, it is essential to understand phage-host ecology. A key step in this ecology is infection of bacterial hosts. Previous work has explored single and multiple, sequential infections. Here we focus on the theory of simultaneous infections, where multiple phages simultaneously attach to and infect one bacterial host cell. Simultaneous infections are a relevant infection dynamic to consider, especially at high phage densities when many phages attach to a single host cell in a short time window. For high bacterial growth rates, simultaneous infection can result in bi-stability: depending on initial conditions phages go extinct or co-exist with hosts, either at stable densities or through periodic oscillations of a stable limit cycle. This bears important consequences for phage applications such as phage therapy: phages can persist even though they cannot invade. Consequently, through spikes in phage densities it is possible to infect a bacterial population even when the phage basic reproductive number is less than one. In the regime of stable limit cycles, if timed right, only small densities of phage may be necessary.

Ion Ciobotari, Adriana Príncipe, Maria Alexandra Oliveira et al.

The collection of ecological data in the field is essential to diagnose, monitor and manage ecosystems in a sustainable way. Since acquisition of this information through traditional methods are generally time-consuming, due to the capability of recording large volumes of data in short time periods, automation of data acquisition sees a growing trend. Terrestrial laser scanners (TLS), particularly LiDAR sensors, have been used in ecology, allowing to reconstruct the 3D structure of vegetation, and thus, infer ecosystem characteristics based on the spatial variation of the density of points. However, the low amount of information obtained per beam, lack of data analysis tools and the high cost of the equipment limit their use. This way, a low-cost TLS (<10k$) was developed along with data acquisition and processing mechanisms applicable in two case studies: an urban garden and a target area for ecological restoration. The orientation of LiDAR was modified to make observations in the vertical plane and a motor was integrated for its rotation, enabling the acquisition of 360 degree data with high resolution. Motion and location sensors were also integrated for automatic error correction and georeferencing. From the data generated, histograms of point density variation along the vegetation height were created, where shrub stratum was easily distinguishable from tree stratum, and maximum tree height and shrub cover were calculated. These results agreed with the field data, whereby the developed TLS has proved to be effective in calculating metrics of structural complexity of vegetation.

Juan Giral Martínez

Disordered systems theory provides powerful tools to analyze the generic behaviors of highdimensional systems, such as species-rich ecological communities or neural networks. By assuming randomness in their interactions, universality ensures that many microscopic details are irrelevant to system-wide dynamics; but the choice of a random ensemble still limits the generality of results. We show here, in the context of ecological dynamics, that these analytical tools do not require a specific choice of ensemble, and that solutions can be found based only on a fundamental rotational symmetry in the interactions, encoding the idea that traits can be recombined into new species without altering global features. Dynamical outcomes then depend on the spectrum of the interaction matrix as a free parameter, allowing us to bridge between results found in different models of interactions, and extend beyond them to previously unidentified behaviors. The distinctive feature of ecological models is the possibility of species extinctions, which leads to an increased universality of dynamics as the fraction of extinct species increases. We expect that these findings can inform new developments in theoretical ecology as well as for other families of complex systems.