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.

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.

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.

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.

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.

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data.

Comprehensive biodiversity data is crucial for ecosystem protection. The Biome mobile app, launched in Japan, efficiently gathers species observations from the public using species identification algorithms and gamification elements. The app has amassed >6 million observations since 2019. Nonetheless, community-sourced data may exhibit spatial and taxonomic biases. Species distribution models (SDMs) estimate species distribution while accommodating such bias. Here, we investigated the quality of Biome data and its impact on SDM performance. Species identification accuracy exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. Our SDMs for 132 terrestrial plants and animals across Japan revealed that incorporating Biome data into traditional survey data improved accuracy. For endangered species, traditional survey data required >2000 records for accurate models (Boyce index ≥ 0.9), while blending the two data sources reduced this to around 300. The uniform coverage of urban-natural gradients by Biome data, compared to traditional data biased towards natural areas, may explain this improvement. Combining multiple data sources better estimates species distributions, aiding in protected area designation and ecosystem service assessment. Establishing a platform for accumulating community-sourced distribution data will contribute to conserving and monitoring natural ecosystems.

The internet has allowed people to share their experiences through images, videos or audio recordings. This has led to the creation of online communities around a variety of topics, including biodiversity. In 2019, a smartphone app, called Biome, was created to fuel biodiversity engagement by making wildlife surveying an easy and fun activity via gamification and assisted species identification through image recognition and ecological analyses. These types of observations are essential for understanding biological communities and species habitats, and they can indicate where and when species occur. Across Japan, Biome has gathered over 6.5 million observations of different species. For biologists, this type of data is extremely useful because it is continuous and enables advanced statistical estimations of species distributions. The fact that the approach is enjoyable to the user also means more people are willing to participate, lowering the barriers to collecting data about biodiversity loss. However, questions remain regarding whether community-sourced data is robust enough for scientific purposes. To address this, Atsumi et al. investigated the quality of occurrence data collected in Biome. The researchers found that community identification of birds, reptiles, mammals and amphibians all exceeded 95% in accuracy. However, the accuracy fell for harder-to-judge seed plants, molluscs and fish species, ranging below 90%. Atsumi et al. also compared how estimated distributions of each species changed when only scientific data was used, versus when it was combined with community data. To perform this analysis, the scientists recognized variations in observation efforts across different locations and individuals and adjusted for these biases in their estimations. They found that adding community-sourced data significantly improved the accuracy of species distribution estimations, including endangered species. Atsumi et al. demonstrate that Biome data is useful when deciding which areas to designate as protected in terms of biodiversity. Additionally, these data can provide guidance for stakeholder-informed ecosystem service assessments. The element of rapid and reliable data collection can contribute to growing positive attitudes towards nature and biodiversity, The platform's community-driven nature also indicates an increase in biodiversity awareness and may link to crafting informative socio-environmental policy commitments.

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.

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.

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.

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.

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?

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.

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.

The world's oceans are home to tens of thousands of fish species, many of which live in nutrient-rich coastal waters. Different species living in a particular environment interact with each other in many ways. For example, a predatory fish may prey on some species of small fish but avoid feeding on others that help it by removing parasites from its skin. Rising ocean temperatures caused by global climate change could affect how different fish species interact with one another and, as a result, impact their communities. One of the first steps to understanding how fish interact with each other in nature typically requires researchers to count the number of different species present and observe how they behave, which is time-consuming and labor-intensive. An alternative is to use an emerging technique in which researchers extract DNA from water, soil or air ­ known as environmental DNA ­ and analyze it to identify the species present and estimate their numbers. Ushio et al. analyzed hundreds of samples of seawater that had been collected over a two-year period from the Boso Peninsula in Japan. Statistical methods were used to quantify how strongly fish species interact with each other and determine whether the temperature of the water influenced how different species of fish interacted over time. The findings showed that water temperature had a significant but complex effect on how strongly pairs of fish species interacted, with both positive and negative effects depending on the conditions. The impact of water temperature on the strength of the interactions varied between species, for example, Japanese anchovy and largescale blackfish interacted less strongly with other fish species in warmer water, whereas the Stripey and a species of wrasse interacted with other fish species more strongly. The findings provide new insights into how water temperature affects the communities of fish living in coastal areas. Alongside complementing existing knowledge in the field, refining the research framework used in this work will benefit those working in fishery science by providing valuable insights into how natural and commercially important fish species respond to climate change.

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.

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.

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).

Quantitative assessment of multiple fish species around artificial reefs combining environmental DNA metabarcoding and acoustic survey.

Since the early 1970s, many artificial reefs (ARs) have been deployed in Japanese coastal waters to create fisheries grounds. Recently, researchers began to use environmental DNA (eDNA) methods for biodiversity monitoring of aquatic species. A metabarcoding approach using internal standard DNAs [i.e., quantitative MiSeq sequencing (qMiSeq)] makes it possible to monitor eDNA concentrations of multiple species simultaneously. This method can improve the efficiency of monitoring AR effects on fishes. Our study investigated distributions of marine fishes at ARs and surrounding stations in the open oceanographic environment of Tateyama Bay, central Japan, using qMiSeq and echo sounder survey. Using the qMiSeq with 12S primers, we found higher quantities of fish eDNAs at the ARs than at surrounding stations and different fish species compositions between them. Comparisons with echo sounder survey also showed positive correlations between fish eDNA concentration and echo intensity, which indicated a highly localized signal of eDNA at each sampling station. These results suggest that qMiSeq is a promising technique to complement conventional methods to monitor distributions of multiple fish species.