Collection of environmental DNA from stemflow for monitoring arboreal biodiversity: Preliminary validation using lichens

The forest canopy harbors a diverse array of organisms. However, monitoring their biodiversity poses challenges due to limited accessibility and the vast taxonomic diversity. To address these challenges, we present a novel method for capturing arboreal biodiversity by harnessing stemflow as a source of DNA from organisms inhabiting trees. Our method involves encircling the tree trunk with gauze, directing the stemflow along the gauze into a funnel, and collecting it in a plastic bag. We employed dual collection systems to retrieve environmental DNA (eDNA) from the stemflow: the gauze trap, designed to capture macroscopic biological fragments, and the plastic bag trap, which collected the stemflow itself. The trapped fragments and stemflow were separately filtered, and eDNA was subsequently extracted from the filter membranes. To validate our method, we focused on foliose lichens, which are easily observable on tree surfaces. We performed eDNA metabarcoding and successfully detected a majority of the observed foliose lichen species, including those not identified through visual observation alone.•We have developed a non-invasive and straightforward method for monitoring arboreal biodiversity by collecting eDNA from stemflow, which has been validated using lichens for its efficacy.•This cost-effective approach minimizes disruptions to tree ecosystems and is expected to provide an efficient means of sampling and monitoring arboreal organisms.

Metagenomic analysis of ecological niche overlap and community collapse in microbiome dynamics

Species utilizing the same resources often fail to coexist for extended periods of time. Such competitive exclusion mechanisms potentially underly microbiome dynamics, causing breakdowns of communities composed of species with similar genetic backgrounds of resource utilization. Although genes responsible for competitive exclusion among a small number of species have been investigated in pioneering studies, it remains a major challenge to integrate genomics and ecology for understanding stable coexistence in species-rich communities. Here, we examine whether community-scale analyses of functional gene redundancy can provide a useful platform for interpreting and predicting collapse of bacterial communities. Through 110-day time-series of experimental microbiome dynamics, we analyzed the metagenome-assembled genomes of co-occurring bacterial species. We then inferred ecological niche space based on the multivariate analysis of the genome compositions. The analysis allowed us to evaluate potential shifts in the level of niche overlap between species through time. We hypothesized that community-scale pressure of competitive exclusion could be evaluated by quantifying overlap of genetically determined resource-use profiles (metabolic pathway profiles) among coexisting species. We found that the degree of community compositional changes observed in the experimental microbiome was correlated with the magnitude of gene-repertoire overlaps among bacterial species, although the causation between the two variables deserves future extensive research. The metagenome-based analysis of genetic potential for competitive exclusion will help us forecast major events in microbiome dynamics such as sudden community collapse (i.e., dysbiosis).

An ecological network approach for detecting and validating influential organisms for rice growth

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

Temperature sensitivity of the interspecific interaction strength of coastal marine fish communities

The effects of temperature on interaction strengths are important for understanding and forecasting how global climate change impacts marine ecosystems; however, tracking and quantifying interactions of marine fish species are practically difficult especially under field conditions, and thus, how temperature influences their interaction strengths under field conditions remains poorly understood. We herein performed quantitative fish environmental DNA (eDNA) metabarcoding on 550 seawater samples that were collected twice a month from 11 coastal sites for 2 years in the Boso Peninsula, Japan, and analyzed eDNA monitoring data using nonlinear time series analytical tools. We detected fish-fish interactions as information flow between eDNA time series, reconstructed interaction networks for the top 50 frequently detected species, and quantified pairwise, fluctuating interaction strengths. Although there was a large variation, water temperature influenced fish-fish interaction strengths. The impact of water temperature on interspecific interaction strengths varied among fish species, suggesting that fish species identity influences the temperature effects on interactions. For example, interaction strengths that Halichoeres tenuispinis and Microcanthus strigatus received strongly increased with water temperature, while those of Engraulis japonicus and Girella punctata decreased with water temperature. An increase in water temperature induced by global climate change may change fish interactions in a complex way, which consequently influences marine community dynamics and stability. Our research demonstrates a practical research framework to study the effects of environmental variables on interaction strengths of marine communities in nature, which would contribute to understanding and predicting natural marine ecosystem dynamics.

Non-destructive collection and metabarcoding of arthropod environmental DNA remained on a terrestrial plant

Reliable survey of arthropods is a crucial for their conservation, community ecology, and pest control on terrestrial plants. However, efficient and comprehensive surveys are hindered by challenges in collecting arthropods and identifying especially small species. To address this issue, we developed a non-destructive environmental DNA (eDNA) collection method termed "plant flow collection" to apply eDNA metabarcoding to terrestrial arthropods. This involves spraying distilled or tap water, or using rainfall, which eventually flows over the surface of the plant, and is collected in a container that is set at the plant base. DNA is extracted from collected water and a DNA barcode region of cytochrome c oxidase subunit I (COI) gene is amplified and sequenced using a high-throughput Illumina Miseq platform. We identified more than 64 taxonomic groups of arthropods at the family level, of which 7 were visually observed or artificially introduced species, whereas the other 57 groups of arthropods, including 22 species, were not observed in the visual survey. These results show that the developed method is possible to detect the arthropod eDNA remained on plants although our sample size was small and the sequence size was unevenly distributed among the three water types tested.

Computational capability of ecological dynamics

Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem?

Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

BACKGROUND

Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as "dysbiosis" in human microbiomes.

METHODS

We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure.

RESULTS

We confirmed that the abrupt community changes observed through the time-series could be described as shifts between "alternative stable states" or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the "energy landscape" analysis of statistical physics or that of a stability index of nonlinear mechanics.

CONCLUSIONS

The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. Video Abstract.

Facilitative interaction networks in experimental microbial community dynamics

Facilitative interactions between microbial species are ubiquitous in various types of ecosystems on the Earth. Therefore, inferring how entangled webs of interspecific interactions shift through time in microbial ecosystems is an essential step for understanding ecological processes driving microbiome dynamics. By compiling shotgun metagenomic sequencing data of an experimental microbial community, we examined how the architectural features of facilitative interaction networks could change through time. A metabolic modeling approach for estimating dependence between microbial genomes (species) allowed us to infer the network structure of potential facilitative interactions at 13 time points through the 110-day monitoring of experimental microbiomes. We then found that positive feedback loops, which were theoretically predicted to promote cascade breakdown of ecological communities, existed within the inferred networks of metabolic interactions prior to the drastic community-compositional shift observed in the microbiome time-series. We further applied "directed-graph" analyses to pinpoint potential keystone species located at the "upper stream" positions of such feedback loops. These analyses on facilitative interactions will help us understand key mechanisms causing catastrophic shifts in microbial community structure.

The relationship between eDNA density distribution and current fields around an artificial reef in the waters of Tateyama Bay, Japan

Monitoring of artificial reefs (ARs) has been conducted through such methods as visual censuses, surveys using fishing gear, and echo sounder. These methods have disadvantages: visual census is not possible at ARs in deeper waters, fishing gear surveys are invasive to fish individuals, and echo sounders have difficulty in species identification. A new AR monitoring method is required to compensate for these disadvantages. While eDNA has become a valid monitoring tool for marine biodiversities, it is influenced by degradation and transport of the molecules that affect information about the spatio-temporal distribution of fish. An understanding of the relationship between current fields and eDNA distribution, particularly in open waters, is critical when using eDNA as an index for fish aggregation at ARs. We investigated the relationship between eDNA distribution and current fields around an AR for four dominant species (Engraulis japonicus, Parapristipoma trilineatum, Scomber spp and Trachurus japonicus) in Tateyama Bay, Japan. The highest density of fish schools is formed directly above or at the upstream side of ARs. If we assume that the center of eDNA originates at these locations at an AR and eDNA is simply transported by currents, a higher density of eDNA would distribute downstream from the AR. However, our results indicate that eDNA distribution is in accord with actual fish distribution, namely eDNA densities are more abundant in the upstream side of ARs. We thus consider that eDNA distribution is more influenced by actual distribution patterns than by the transport processes.

Are microbes growing on flowers evil? Effects of old flower microbes on fruit set in a wild ginger with one-day flowers, Alpinia japonica (Zingiberaceae)

Flowers are colonized and inhabited by diverse microbes. Flowers have various mechanisms to suppress microbial growth, such as flower volatiles, reactive oxygen and secondary compounds. Besides, plants rapidly replace flowers that have a short lifespan, and old flowers senesce. They may contribute to avoiding adverse effects of the microbes. In this study, we investigate if the flower microbial community on old flowers impedes fruit and seed production in a wild ginger with one-day flowers. We focus on microbes on old flowers because they may be composed of microbes that would grow during flowering if the flowers did not have mechanisms to suppress microbial growth. We inoculated newly opened flowers with old flower microbes, and monitored the effects on fruit and seed set. We also assessed prokaryotic communities on the flowers using 16S rRNA amplicon sequencing. We found six bacterial amplicon sequence variants (ASVs) whose proportions were increased on the inoculated flowers. These ASVs were also found on flower buds and flowers that were bagged by net or paper during anthesis, suggesting that they had been present in small numbers prior to flowering. Fruit set was negatively associated with the proportions of these ASVs, while seed set was not. The results suggest that old flowers harbor microbial communities different from those at anthesis, and that the microbes abundant on old flowers negatively affect plant reproduction. Although it has received little attention, antagonistic microbes that rapidly proliferate on the flowers may have affected the evolution of various flower characteristics such as flower volatiles and life span.

Interaction capacity as a potential driver of community diversity

How patterns in community diversity emerge is a long-standing question in ecology. Studies suggested that community diversity and interspecific interactions are interdependent. However, evidence from high-diversity ecological communities is lacking because of practical challenges in characterizing speciose communities and their interactions. Here, I analysed time-varying interaction networks that were reconstructed using 1197 species, DNA-based ecological time series taken from experimental rice plots and empirical dynamic modelling, and introduced 'interaction capacity', namely, the sum of interaction strength that a single species gives and receives, as a potential driver of community diversity. As community diversity increases, the number of interactions increases exponentially but the mean interaction capacity of a community becomes saturated, weakening interspecific interactions. These patterns are modelled with simple mathematical equations, based on which I propose the 'interaction capacity hypothesis': that interaction capacity and network connectance can be two fundamental properties that influence community diversity. Furthermore, I show that total DNA abundance and temperature influence interaction capacity and connectance nonlinearly, explaining a large proportion of diversity patterns observed in various systems. The interaction capacity hypothesis enables mechanistic explanations of community diversity. Therefore, analysing ecological community data from the viewpoint of interaction capacity would provide new insight into community diversity.

Reconstructing large interaction networks from empirical time series data

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named "multiview distance regularised S-map," generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.

Evaluation of seasonal dynamics of fungal DNA assemblages in a flow-regulated stream in a restored forest using eDNA metabarcoding

Investigation of seasonal variation in fungal communities is essential for understanding biodiversity and ecosystem functions. However, the conventional sampling method, with substrate removal and high spatial heterogeneity of community composition, makes surveying the seasonality of fungal communities challenging. Recently, water environmental DNA (eDNA) analysis has been explored for its utility in biodiversity surveys. In this study, we assessed whether the seasonality of fungal communities can be detected by monitoring eDNA in a forest stream. We conducted monthly water sampling in a forest stream over 2 years and used DNA metabarcoding to identify fungal eDNA. The stream water contained DNA from functionally diverse aquatic and terrestrial fungi, such as plant decomposers, parasites and mutualists. The variation in the fungal assemblage showed a regular annual periodicity, meaning that the assemblages in a given season were similar, irrespective of the year or sampling. Furthermore, the strength of the annual periodicity varied among functional groups. Our results suggest that forest streams may act as a 'trap' for terrestrial fungal DNA derived from different habitats, allowing the analysis of fungal DNA in stream water to provide information about the temporal variation in fungal communities in both the aquatic and the surrounding terrestrial ecosystems.

Causal analysis of the temperature impact on deep-sea biodiversity

The deep sea comprises more than 90% of the ocean; therefore, understanding the controlling factors of biodiversity in the deep sea is of great importance for predicting future changes in the functioning of the ocean system. Consensus has recently been increasing on two plausible factors that have often been discussed as the drivers of deep-sea species richness in the contexts of the species-energy and physiological tolerance hypotheses: (i) seafloor particulate organic carbon (POC) derived from primary production in the euphotic zone and (ii) temperature. Nonetheless, factors that drive deep-sea biodiversity are still actively debated potentially owing to a mirage of correlations (sign and magnitude are generally time dependent), which are often found in nonlinear, complex ecological systems, making the characterization of causalities difficult. Here, we tested the causal influences of POC flux and temperature on species richness using long-term palaeoecological datasets derived from sediment core samples and convergent cross mapping, a numerical method for characterizing causal relationships in complex systems. The results showed that temperature, but not POC flux, influenced species richness over 10³-10⁴-year time scales. The temperature-richness relationship in the deep sea suggests that human-induced future climate change may, under some conditions, affect deep-sea ecosystems through deep-water circulation changes rather than surface productivity changes.

Are networks of trophic interactions sufficient for understanding the dynamics of multi-trophic communities? Analysis of a tri-trophic insect food-web time-series

Resource-consumer interactions are considered a major driving force of population and community dynamics. However, species also interact in many non-trophic and indirect ways and it is currently not known to what extent the dynamic coupling of species corresponds to the distribution of trophic links. Here, using a 10-year data set of monthly observations of a 40-species tri-trophic insect community and nonlinear time series analysis, we compare the occurrence and strengths of both the trophic and dynamic interactions in the insect community. The matching between observed trophic and dynamic interactions provides evidence that population dynamic interactions reflect resource-consumer interactions in the many-species community. However, the presence of a trophic interaction does not always correspond to a detectable dynamic interaction especially for top-down effects. Moreover a considerable proportion of dynamic interactions are not attributable to direct trophic interactions, suggesting the unignorable role of non-trophic and indirect interactions as co-drivers of community dynamics.

Quantitative evaluation of intraspecific genetic diversity in a natural fish population using environmental DNA analysis

Recent advances in environmental DNA (eDNA) analysis using high-throughput sequencing (HTS) enable evaluation of intraspecific genetic diversity in a population. As the intraspecific genetic diversity provides invaluable information for wildlife conservation and management, there is an increasing demand to apply eDNA analysis to population genetics and the phylogeography by quantitative evaluation of intraspecific diversity. However, quantitative evaluations of intraspecific genetic diversity using eDNA is not straightforward because the number of eDNA sequence reads obtained by HTS may not be an index of the quantity of eDNA. In this study, to quantitatively evaluate genetic diversity using eDNA analysis, we applied a quantitative eDNA metabarcoding method using the internal standard DNAs. We targeted Ayu (Plecoglossus altivelis altivelis) and added internal standard DNAs with known copy numbers to each eDNA sample obtained from three rivers during the library preparation process. The sequence reads of each Ayu haplotype were successfully converted to DNA copy numbers based on the relationship between the copy numbers and sequence reads of the internal standard DNAs. In all rivers, the calculated copy number of each haplotype showed a significant positive correlation with the haplotype frequency estimated by a capture-based survey. Furthermore, estimates of genetic indicators such as nucleotide diversity based on the eDNA copy numbers were comparable with those estimated based on a capture-based study. Our results demonstrate that eDNA analysis with internal standard DNAs enables reasonable quantification of intraspecific genetic diversity, and this method could thus be a promising tool in the field of population genetics and phylogeography.

Environmental DNA analysis shows high potential as a tool for estimating intraspecific genetic diversity in a wild fish population

Environmental DNA (eDNA) analysis has recently been used as a new tool for estimating intraspecific diversity. However, whether known haplotypes contained in a sample can be detected correctly using eDNA-based methods has been examined only by an aquarium experiment. Here, we tested whether the haplotypes of Ayu fish (Plecoglossus altivelis altivelis) detected in a capture survey could also be detected from an eDNA sample derived from the field that contained various haplotypes with low concentrations and foreign substances. A water sample and Ayu specimens collected from a river on the same day were analysed by eDNA analysis and Sanger sequencing, respectively. The 10 L water sample was divided into 20 filters for each of which 15 PCR replications were performed. After high-throughput sequencing, denoising was performed using two of the most widely used denoising packages, unoise3 and dada2. Of the 42 haplotypes obtained from the Sanger sequencing of 96 specimens, 38 (unoise3) and 41 (dada2) haplotypes were detected by eDNA analysis. When dada2 was used, except for one haplotype, haplotypes owned by at least two specimens were detected from all the filter replications. Accordingly, although it is important to note that eDNA-based method has some limitations and some risk of false positive and false negative, this study showed that the eDNA analysis for evaluating intraspecific genetic diversity provides comparable results for large-scale capture-based conventional methods. Our results suggest that eDNA-based methods could become a more efficient survey method for investigating intraspecific genetic diversity in the field.

A data-driven approach to complex ecological systems

Interspecific interaction has been a key concept in ecology to understand the structure and dynamics of ecological communities. Important, yet often overlooked, is that an interspecific interaction is a product of multiple biological processes at various temporal and spatial scales, including changes in demographic parameters such as birth and death rates, behavioral responses such as inter-habitat movements, and hiding and evolutionary responses in a longer temporal scale. Each of those mechanisms, according to ecological theory, potentially affects population dynamics and modifies the community-level properties such as community complexity and stability in different manners. Here, a question arises: how does the net interspecific interaction, which is made up with those multiple processes, look like in the real nature? How do changes depend on the temporal or spatial scale? In this chapter we show that a data-driven approach using demographic time series is a powerful tool to answer those questions. According to nonlinear dynamics theory, a time series of a variable contains information about the dynamic system that the variable belongs to. We can use this fact to identify interspecific interactions, quantify their signs and strengths and evaluate its effect to community-level dynamic properties. Some results we got by applying the time-series analysis based on nonlinear dynamics theory (called Empirical Dynamic Modeling) to empirical demographic data, experimental or observational, will be presented, which will demonstrate how fluctuating and condition-dependent the real interactions are and reveal how those interactions give rise to the dynamic properties at higher organization levels.

Repressive chromatin modification underpins the long-term expression trend of a perennial flowering gene in nature

Natural environments require organisms to possess robust mechanisms allowing responses to seasonal trends. In Arabidopsis halleri, the flowering regulator AhgFLC shows upregulation and downregulation phases along with long-term past temperature, but the underlying machinery remains elusive. Here, we investigate the seasonal dynamics of histone modifications, H3K27me3 and H3K4me3, at AhgFLC in a natural population. Our advanced modelling and transplant experiments reveal that H3K27me3-mediated chromatin regulation at AhgFLC provides two essential properties. One is the ability to respond to the long-term temperature trends via bidirectional interactions between H3K27me3 and H3K4me3; the other is the ratchet-like character of the AhgFLC system, i.e. reversible in the entire perennial life cycle but irreversible during the upregulation phase. Furthermore, we show that the long-term temperature trends are locally indexed at AhgFLC in the form of histone modifications. Our study provides a more comprehensive understanding of H3K27me3 function at AhgFLC in a complex natural environment.

The flowering regulator FLC shows upregulation and downregulation phases along with long-term past temperature in Arabidopsishalleri. Here, the authors reveal that H3K27me3-mediated chromatin regulation at AhgFLC provides the ability to respond to both the seasonal temperature trends and the perennial life cycle.

Complementary molecular methods reveal comprehensive phylogenetic diversity integrating inconspicuous lineages of early-diverged wood-decaying mushrooms

Among terrestrial microorganisms, mushroom-forming fungi have been relatively well investigated, however the inconspicuous strains may be overlooked by conventional visual investigations causing underestimation of their phylogenetic diversity. Herein, we sought to obtain a comprehensive phylogenetic diversity profile for the early-diverging wood-decaying mushrooms Dacrymycetes, using an approach that combines fruiting-body collection, culture isolation, and environmental DNA (eDNA) metabarcoding of decaying branches. Among the 28 operational taxonomic units (OTUs) detected during a three-year investigation, 10 each were from fruiting bodies and cultured mycelia and 27 were detected as eDNA sequences. eDNA metabarcoding revealed various lineages across the Dacrymycetes phylogeny. Alternatively, fruiting-body and culture surveys uncovered only ~50% of the OTUs detected through eDNA metabarcoding, suggesting that several inconspicuous or difficult-to-isolate strains are latent in the environment. Further, eDNA and culture surveys revealed early-diverging clades that were not identified in the fruiting-body survey. Thus, eDNA and culture-based techniques can uncover inconspicuous yet phylogenetically important mushroom lineages that may otherwise be overlooked via typical visual investigations.

Publisher Correction: Core microbiomes for sustainable agroecosystems

Owing to a technical error, this Perspective was originally published without its received and accepted dates; the dates "Received: 31 December 2017; Accepted: 23 March 2018" have now been included in all versions.

Core microbiomes for sustainable agroecosystems

In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of 'core microbiomes', which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.

Fluctuating interaction network and time-varying stability of a natural fish community

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Environmental DNA enables detection of terrestrial mammals from forest pond water

Terrestrial animals must have frequent contact with water to survive, implying that environmental DNA (eDNA) originating from those animals should be detectable from places containing water in terrestrial ecosystems. Aiming to detect the presence of terrestrial mammals using forest water samples, we applied a set of universal PCR primers (MiMammal, a modified version of fish universal primers) for metabarcoding mammalian eDNA. The versatility of MiMammal primers was tested in silico and by amplifying DNAs extracted from tissues. The results suggested that MiMammal primers are capable of amplifying and distinguishing a diverse group of mammalian species. In addition, analyses of water samples from zoo cages of mammals with known species composition suggested that MiMammal primers could successfully detect mammalian species from water samples in the field. Then, we performed an experiment to detect mammals from natural ecosystems by collecting five 500-ml water samples from ponds in two cool-temperate forests in Hokkaido, northern Japan. MiMammal amplicon libraries were constructed using eDNA extracted from water samples, and sequences generated by Illumina MiSeq were subjected to data processing and taxonomic assignment. We thereby detected multiple species of mammals common to the sampling areas, including deer (Cervus nippon), mouse (Mus musculus), vole (Myodes rufocanus), raccoon (Procyon lotor), rat (Rattus norvegicus) and shrew (Sorex unguiculatus). Many previous applications of the eDNA metabarcoding approach have been limited to aquatic/semiaquatic systems, but the results presented here show that the approach is also promising even for forest mammal biodiversity surveys.

Water temperature-dependent degradation of environmental DNA and its relation to bacterial abundance

Environmental DNA (eDNA) is DNA shed by organisms into surrounding environments such as soil and water. The new methods using eDNA as a marker for species detection are being rapidly developed. Here we explore basic knowledge regarding the dependence of the eDNA degradation rate on time and water temperature, and the relationship between eDNA degradation and bacterial abundance. This subject has not been well clarified, even though it is essential for improving the reliability of eDNA analysis. To determine the time- and water temperature-dependent degradation of eDNA, river water was sampled and eDNA concentrations were determined for ayu sweetfish (Plecoglossus altivelis altivelis) and common carp (Cyprinus carpio) at seven time points, over a 48-h period, and at three different water temperatures. The degradation of eDNA was modeled for each species using an existing exponential decay model with an extension to include water temperature effects. The degradation models were constructed for ayu sweetfish as N_t = 229,901.2 × exp [− (0.01062 × k − 0.07081) × t] and for common carp as N_t = 2,558.0 × exp [− (0.01075 × k − 0.07372) × t]. N_t is the DNA concentration at time t (elapsed time in hours) and k is the water temperature (°C). We also measured the concentration of eDNA derived from purified genomic DNA of the common carp, which was spiked into aquarium water without the target species, and we measured the bacterial abundance in the sample water after 12 and 24 h of incubation. Environmental DNA degradation was accelerated at higher water temperatures (generalized linear model, GLM; p < 0.001), but bacterial abundance did not have a significant effect on eDNA degradation (GLM, p = 0.097). These results suggest that the proper treatment of this temperature effect in data interpretations and adjustments would increase the reliability of eDNA analysis in future studies.

A lognormal distribution of the lengths of terminal twigs on self-similar branches of elm trees

Lognormal distributions and self-similarity are characteristics associated with a wide range of biological systems. The sequential breakage model has established a link between lognormal distributions and self-similarity and has been used to explain species abundance distributions. To date, however, there has been no similar evidence in studies of multicellular organismal forms. We tested the hypotheses that the distribution of the lengths of terminal stems of Japanese elm trees (Ulmus davidiana), the end products of a self-similar branching process, approaches a lognormal distribution. We measured the length of the stem segments of three elm branches and obtained the following results: (i) each occurrence of branching caused variations or errors in the lengths of the child stems relative to their parent stems; (ii) the branches showed statistical self-similarity; the observed error distributions were similar at all scales within each branch and (iii) the multiplicative effect of these errors generated variations of the lengths of terminal twigs that were well approximated by a lognormal distribution, although some statistically significant deviations from strict lognormality were observed for one branch. Our results provide the first empirical evidence that statistical self-similarity of an organismal form generates a lognormal distribution of organ sizes.

Microbial communities on flower surfaces act as signatures of pollinator visitation

Microbes are easily dispersed from one place to another, and immigrant microbes might contain information about the environments from which they came. We hypothesized that part of the microbial community on a flower's surface is transferred there from insect body surfaces and that this community can provide information to identify potential pollinator insects of that plant. We collected insect samples from the field, and found that an insect individual harbored an average of 12.2 × 10⁵ microbial cells on its surface. A laboratory experiment showed that the microbial community composition on a flower surface changed after contact with an insect, suggesting that microbes are transferred from the insect to the flower. Comparison of the microbial fingerprint approach and direct visual observation under field condition suggested that the microbial community on a flower surface could to some extent indicate the structure of plant–pollinator interactions. In conclusion, species-specific insect microbial communities specific to insect species can be transferred from an insect body to a flower surface, and these microbes can serve as a “fingerprint” of the insect species, especially for large-bodied insects. Dispersal of microbes is a ubiquitous phenomenon that has unexpected and novel applications in many fields and disciplines.

Local increase in IgE and class switch recombination to IgE in nasal polyps in chronic rhinosinusitis

BACKGROUND

Chronic rhinosinusitis with nasal polyps is generally characterized by local Th2 inflammation and is categorized into two subtypes in Japan: eosinophilic chronic rhinosinusitis (similar to chronic rhinosinusitis with nasal polyps in western countries) and non-eosinophilic chronic rhinosinusitis (characterized by Th1-dominant inflammation).

OBJECTIVE

To investigate local IgE production and class switch recombination to IgE in these two subtypes of chronic rhinosinusitis with nasal polyps.

METHODS

The identity of IgE-positive cells was determined using double-immunofluorescent staining for IgE and cell-type-specific molecular markers. To investigate the local class switch recombination to IgE and IgE synthesis in the mucosa, we performed real-time polymerase chain reaction to examine the mRNA expression of Th2 cytokines and class-switch-related molecules, including IL-4, IL-5, IL-13, ε germline gene transcripts, IgE mature transcript, IgG mature transcript, RAG1, RAG2 and activation-induced cytidine deaminase in eosinophilic polyps, non-eosinophilic polyps and controls.

RESULTS

The concentrations of total IgE and number of IgE-positive cells were significantly higher in the eosinophilic polyps compared with control and non-eosinophilic polyps. IgE-positive cells were predominantly mast cells in eosinophilic polyps and significantly correlated with the number of FcεR1-positive cells in the subepithelial layer. IL-5 and IL-13 mRNA and ε germline gene transcripts expression levels were significantly higher in eosinophilic polyps compared with control and non-eosinophilic polyps. In contrast, the number of plasma cells and the expression of IgG mature transcripts were increased in non-eosinophilic polyps compared with eosinophilic polyps. RAG2 mRNA was significantly increased in both eosinophilic and non-eosinophilic polyps compared with control mucosa.

CONCLUSION AND CLINICAL RELEVANCE

The current study suggests local class switching to IgE, production of IgE and IgE localization to the surface of mast cells in eosinophilic chronic rhinosinusitis in the Japanese population. The difference in the IgE-related profiles between eosinophilic chronic rhinosinusitis and non-eosinophilic chronic rhinosinusitis suggests heterogeneity in the pathogenesis of chronic rhinosinusitis with nasal polyps.

A coexisting fungal-bacterial community stabilizes soil decomposition activity in a microcosm experiment

How diversity influences the stability of a community function is a major question in ecology. However, only limited empirical investigations of the diversity–stability relationship in soil microbial communities have been undertaken, despite the fundamental role of microbial communities in driving carbon and nutrient cycling in terrestrial ecosystems. In this study, we conducted a microcosm experiment to investigate the relationship between microbial diversity and stability of soil decomposition activities against changes in decomposition substrate quality by manipulating microbial community using selective biocides. We found that soil respiration rates and degradation enzyme activities by a coexisting fungal and bacterial community (a taxonomically diverse community) are more stable against changes in substrate quality (plant leaf materials) than those of a fungi-dominated or a bacteria-dominated community (less diverse community). Flexible changes in the microbial community composition and/or physiological state in the coexisting community against changes in substrate quality, as inferred by the soil lipid profile, may be the mechanism underlying this positive diversity–stability relationship. Our experiment demonstrated that the previously found positive diversity–stability relationship could also be valid in the soil microbial community. Our results also imply that the functional/taxonomic diversity and community ecology of soil microbes should be incorporated into the context of climate–ecosystem feedbacks. Changes in substrate quality, which could be induced by climate change, have impacts on decomposition process and carbon dioxide emission from soils, but such impacts may be attenuated by the functional diversity of soil microbial communities.

Predator-prey body size relationships when predators can consume prey larger than themselves

As predator-prey interactions are inherently size-dependent, predator and prey body sizes are key to understanding their feeding relationships. To describe predator-prey size relationships (PPSRs) when predators can consume prey larger than themselves, we conducted field observations targeting three aquatic hemipteran bugs, and assessed their body masses and those of their prey for each hunting event. The data revealed that their PPSR varied with predator size and species identity, although the use of the averaged sizes masked these effects. Specifically, two predators had slightly decreased predator-prey mass ratios (PPMRs) during growth, whereas the other predator specialized on particular sizes of prey, thereby showing a clear positive size-PPMR relationship. We discussed how these patterns could be different from fish predators swallowing smaller prey whole.

Dynamic scaling in the growth of a non-branching plant, Cardiocrinum cordatum

We investigated whole-plant leaf area in relation to ontogenetic variation in leaf-size for a forest perennial herb, Cardiocrinum cordatum. The 200-fold ontogenetic variability in C. cordatum leaf area followed a power-law dependence on total leaf number, a measure of developmental stage. When we normalized for plant size, the function describing the size of single leaves along the stem was similar among different-sized plants, implying that the different-sized canopies observed at different times in the growth trajectory were fundamentally similar to each other. We conclude that the growth trajectory of a population of C. cordatum plant leaves obeyed a dynamic scaling law, the first reported for a growth trajectory at the whole-plant level.

In situ enzyme activity in the dissolved and particulate fraction of the fluid from four pitcher plant species of the genus Nepenthes

The genus Nepenthes, a carnivorous plant, has a pitcher to trap insects and digest them in the contained fluid to gain nutrient. A distinctive character of the pitcher fluid is the digestive enzyme activity that may be derived from plants and dwelling microbes. However, little is known about in situ digestive enzymes in the fluid. Here we examined the pitcher fluid from four species of Nepenthes. High bacterial density was observed within the fluids, ranging from 7×10(6) to 2.2×10(8) cells ml(-1). We measured the activity of three common enzymes in the fluid: acid phosphatases, β-D-glucosidases, and β-D-glucosaminidases. All the tested enzymes detected in the liquid of all the pitcher species showed activity that considerably exceeded that observed in aquatic environments such as freshwater, seawater, and sediment. Our results indicate that high enzyme activity within a pitcher could assist in the rapid decomposition of prey to maximize efficient nutrient use. In addition, we filtered the fluid to distinguish between dissolved enzyme activity and particle-bound activity. As a result, filtration treatment significantly decreased the activity in all enzymes, while pH value and Nepenthes species did not affect the enzyme activity. It suggested that enzymes bound to bacteria and other organic particles also would significantly contribute to the total enzyme activity of the fluid. Since organic particles are themselves usually colonized by attached and highly active bacteria, it is possible that microbe-derived enzymes also play an important role in nutrient recycling within the fluid and affect the metabolism of the Nepenthes pitcher plant.

Functional diversity of microbial decomposers facilitates plant coexistence in a plant-microbe-soil feedback model

Theory and empirical evidence suggest that plant-soil feedback (PSF) determines the structure of a plant community and nutrient cycling in terrestrial ecosystems. The plant community alters the nutrient pool size in soil by affecting litter decomposition processes, which in turn shapes the plant community, forming a PSF system. However, the role of microbial decomposers in PSF function is often overlooked, and it remains unclear whether decomposers reinforce or weaken litter-mediated plant control over nutrient cycling. Here, we present a theoretical model incorporating the functional diversity of both plants and microbial decomposers. Two fundamental microbial processes are included that control nutrient mineralization from plant litter: (i) assimilation of mineralized nutrient into the microbial biomass (microbial immobilization), and (ii) release of the microbial nutrients into the inorganic nutrient pool (net mineralization). With this model, we show that microbial diversity may act as a buffer that weakens plant control over the soil nutrient pool, reversing the sign of PSF from positive to negative and facilitating plant coexistence. This is explained by the decoupling of litter decomposability and nutrient pool size arising from a flexible change in the microbial community composition and decomposition processes in response to variations in plant litter decomposability. Our results suggest that the microbial community plays a central role in PSF function and the plant community structure. Furthermore, the results strongly imply that the plant-centered view of nutrient cycling should be changed to a plant-microbe-soil feedback system, by incorporating the community ecology of microbial decomposers and their functional diversity.

Phenolic control of plant nitrogen acquisition through the inhibition of soil microbial decomposition processes: a plant-microbe competition model

Phenolics are a dominant class of plant secondary metabolites that have strong effects on various ecosystem processes. The ecological significance of these compounds, however, is still poorly understood. We hypothesized that the inhibitory effects of phenolics on microbial activity could enhance plant nitrogen acquisition by relaxing competition between plants and microbes. To test this hypothesis theoretically, we constructed a novel and simple mechanistic model by unifying two concepts: one is a new paradigm of nitrogen cycling which considers the uptake of organic nitrogen by plants, and the other is that phenolics can regulate nitrogen cycling by inhibiting microbial decomposition processes. Our plant-microbe competition model consists of five compartments (plants, soil microbes, debris, organic nitrogen and inorganic nitrogen) and incorporates the essential processes of nitrogen cycling: plant uptake of monomers, competition between plants and microbes, and the depolymerization process. Our analysis showed that plant nitrogen acquisition was maximized at intermediate levels of phenolics, but only when plants could utilize organic nitrogen. Furthermore, this pattern occurred over a broad range of parameter conditions. Our study successfully demonstrated the potential role of phenolics in plant nitrogen acquisition throughout natural environments.