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Ushio, M.

Publications and source records attributed to Ushio, M..

At least 19 recordsLinked to original sources

Population-level, state-dependent response as a trait predicting species redistribution under climate change

Species change their population sizes and distributions in response to fluctuating environments. Predicting such changes across space, particularly distributional shifts under climate change, is a central challenge in ecology and conservation1. Traditionally, static traits such as habitat preferences, or performance traits such as abundance-environment relationships, have been used to characterize species responses to the environment2,3. However, these approaches assume fixed relationships between species and their environments, overlooking the inherently state-dependent nature of ecosystems. Here, we show that population-level, state-dependent responses of species to environments can serve as a novel trait to better represent population dynamics in nature, which we term a dynamic response trait. Using nonlinear time-series analysis4,5 and long-term marine fish community data collected from Kyoto, Japan6, we identify causal influences of water temperature on about one hundred fish species. Species with higher latitudinal centers generally show negative dynamic responses to warming, whereas those with lower latitudinal centers show positive ones. Intriguingly, these dynamic response traits quantified at a single location explain the fish species poleward range shift velocities estimated from public biodiversity records; species with negative dynamic responses to warming shift poleward more rapidly, whereas those with positive ones tend to remain. Our findings establish dynamic response traits as a new dimension of trait-based ecology, capturing state-dependent species responses. By linking local population dynamics to broad-scale distributional shifts, this approach provides a powerful basis for guiding ecology and conservation under climate change.

ecology↗

Exploring causal factors of coastal chlorophyll-a dynamics and their potential contributions to near future forecasting

Harmful algal blooms have been causing significant damage worldwide, and Hong Kong is no exception. To understand the drivers of algal bloom formation and forecast the dynamics of chlorophyll-a (Chl-a), a proxy for algal abundance, in Hong Kong waters, this study utilized nonlinear time series analysis, called empirical dynamic modeling (EDM), to investigate Chl-a dynamics using in situ measurements and remote sensing data. We first conducted causality tests of EDM to identify environmental factors influencing Chl-a at different sites. As for the in situ measurement data, salinity was the strongest causal factor among environmental factors. However, inputting the causal factors into the forecasting model did not greatly improve the forecasting performance for Chl-a, suggesting that factors not included in the current dataset, such as wind direction and current speed, may play a more critical role in Chl-a dynamics. As for the remote sensing data, sea surface temperature (SST) showed significant causal effect on Chl-a at most sites and the multivariate forecasting model including Chl-a and SST outperformed the univariate model at most sites. This study is the first to employ EDM to investigate Chl-a dynamics in Hong Kong waters, showcasing its potential to identify causal factors and improve forecasting accuracy. The findings provide scientific insights into Chl-a dynamics and water quality monitoring and modeling in a coastal region.

ecology↗

Microbiome-host interactions drive a trade-off between sleep quality and lifespan in Drosophila

Understanding the interactions between various aging processes and the resulting heterogeneity in aging is crucial for promoting healthy aging. Here, we provide evidence that heterogeneity in microbiome and host interactions contributes to diversifying aging phenotypes in sleep, gut integrity, and longevity in Drosophila. Aged flies exhibiting sleep fragmentation preserve gut integrity, accompanied by a shift in microbiota composition, particularly an increase in Acinetobacter junii. A. junii induces sleep fragmentation via its metabolite, urocanic acid, through serotonin receptor-dependent dopamine upregulation. In parallel, A. junii exploits the host response to promote its growth, leading to lifespan extension, which is recapitulated by genetically modified Escherichia coli, suggesting a trade-off between sleep quality and lifespan. Our study demonstrates a systematic mechanism underlying aging heterogeneity, suggesting interventions through bacterial supplements.

animal behavior and cognition↗

Detecting the Indo-Pacific finless porpoises (Neophocaena phocaenoides) in Hong Kong waters using environmental DNA

The Indo-Pacific finless porpoise (Neophocaena phocaenoides), a coastal cetacean species widely distributed from the Taiwan Strait to the Persian Gulf, is an iconic species that may represent an overall status indicator of marine ecosystems. However, growing anthropogenic pressures have led to declines in the population of this species in many regions such as Hong Kong waters, highlighting the need for more efficient and reliable monitoring methods. In this study, we applied two environmental DNA (eDNA) analysis methods, species-specific quantitative PCR (qPCR) and cetacean-targeting metabarcoding using {micro}Ceta primers, to detect Indo-Pacific finless porpoises eDNA in Hong Kong waters. First, we empirically validated the performance of the qPCR primers previously designed in another study. Then, in March 2025, water samples were collected from surface and bottom water layers in 20 locations in the Soko Islands area, a major habitat of the Indo-Pacific finless porpoises. The qPCR analysis detected the finless porpoise eDNA in eight out of 40 samples, but all of the eDNA concentrations were below the limit of quantification. {micro}Ceta metabarcoding detected the finless porpoise eDNA from seven out of 40 samples. The qPCR and {micro}Ceta metabarcoding approaches showed comparable detection rates for Indo-Pacific finless porpoise. Interestingly, the {micro}Ceta metabarcoding method detected a transient cetacean species, the false killer whale (Pseudorca crassidens), from a single location. Both methods showed consistent detection rates between surface and bottom water layers, with no statistically significant difference. Overall, the two eDNA analysis methods successfully detected the finless porpoise eDNA in Hong Kong waters, demonstrating the potential of these approaches for monitoring the Indo-Pacific finless porpoises and other cetaceans in this region. Taken together, our findings provide a basis for an eDNA-based cetacean monitoring framework in Hong Kong waters.

ecology↗

Detection of environmental DNA of the Indo-Pacific humpback dolphins in Hong Kong waters using quantitative PCR

Environmental DNA (eDNA) analysis is a promising method to enhance the sensitivity and efficiency of biodiversity monitoring. In Hong Kong, the Indo-Pacific humpback dolphin (Sousa chinensis) is an iconic marine mammal inhabiting shallow coastal regions, playing a vital role in the local biodiversity. However, their population is declining, highlighting the need for effective monitoring methods for S. chinensis. In this study, we developed a species-specific qPCR assay for detecting S. chinensis eDNA. We designed primers and a probe targeting the Cytb region of mitochondrial DNA. Specificity tests and evaluations of the limit of detection and quantification demonstrated that the primers and probe possess sufficient specificity and sensitivity to detect S. chinensis eDNA. Our qPCR method was further validated by detecting S. chinensis eDNA in water samples collected from areas where S. chinensis individuals were sighted. Analysis of 56 coastal water samples collected over two seasons revealed that S. chinensis utilizes the western and southern Lantau regions of Hong Kong waters. With further enhancements to the eDNA-based survey method, including larger water volumes, broader spatial coverage, and targeting other cetaceans and fish, our framework will aid in the conservation of S. chinensis in Hong Kong waters.

ecology↗

Contrasting ecological communities in rice paddy fields under conventional and no-fertilizer farming practices

Global crop production is essential for providing energy and nutrients to humans, but agricultural systems contribute substantially to environmental issues, making it crucial to find sustainable methods for crop production. In this study, we conducted extensive field monitoring of two rice paddy fields in Kyoto Prefecture, Japan, where a rice variety, Hinohikari, was cultivated to investigate how ecological communities influence rice growth and yields under two contrasting farming practices: conventional farming and no-fertilizer farming. We had three objectives: (1) to monitor rice growth and ecological dynamics to create a comprehensive ecological time series using quantitative environmental DNA metabarcoding and complementary monitoring methods, (2) to identify ecological variables that causally affect rice performance using nonlinear time series analysis, and (3) to examine the effects of the two farming practices on ecological variables and rice performance. As expected, the no-fertilizer paddy field showed lower rice growth and yields but still produced 40-50% of the yields of the conventional field. Ecological monitoring revealed contrasting ecological communities between the two fields, particularly among plant species on the paddy ridges and microbial taxa. Twenty-five taxa had statistically clear causal influences on rice performance. While the per-abundance influences of the causal taxa were largely similar in both paddy fields, their abundances were different, contributing to the differences in the overall effects of these taxa on the rice performance. These findings suggest that the abundance of causal taxa, along with nutrient conditions, may have driven differences in rice growth between the two paddy fields.

ecology↗

μCeta: a set of cetacean-specific primers for environmental DNA metabarcoding with minimal amplification of non-target vertebrates

Biodiversity monitoring is crucial for understanding ecosystem dynamics and species distributions, particularly in the context of anthropogenic impacts and climate change. Cetaceans, as key indicator species of marine ecosystems, face increasing threats from human activities, highlighting the need for effective, non-invasive monitoring methods. In the present study, we developed novel Cetacea-specific primer sets to enhance the detection efficiency of cetacean species through environmental DNA (eDNA) metabarcoding, while minimizing the amplification of non-target vertebrates, such as fish and humans. We retrieved mitochondrial genomes of 71 cetacean species from a public database and designed 20 candidate primer sets, which were assessed in silico for their specificity and capacity to differentiate cetacean sequences. Four primer sets with the best in silico performance were selected for empirical validation using DNA from tissue samples and eDNA from seawater collected from aquarium pools and Hong Kong coastal waters. All four primer sets effectively amplified cetacean DNA from tissue samples. However, in the aquarium pool tests, three primer sets failed to accurately identify one or more cetacean species due to a lack of interspecific variation within the amplified region. From these, we selected one primer set targeting a 267 bp region of the mitochondrial 12S rRNA gene, named {micro}Ceta, and applied it to water samples collected from Hong Kong coastal waters, where the iconic Indo-Pacific humpback dolphin (Sousa chinensis) was observed. {micro}Ceta successfully detected S. chinensis eDNA while avoiding amplification of non-target species such as fish or humans. Our results demonstrate that {micro}Ceta is a reliable tool for cetacean eDNA detection in Hong Kong waters, contributing to cetacean conservation and enhancing our understanding of marine biodiversity.

ecology↗

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating citizen 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 >6M 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, mollusks, 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 >2,000 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.

ecology↗

A unified framework for nonparametric causality detection

Most complex systems in the real world are driven by multiple interactions among components. Identifying these interactions is critical for understanding, forecasting, and controlling system-level phenomena. Transfer entropy (TE) and convergent cross mapping (CCM) are two widely-used approaches for nonparametric causality detection based on time-series data. However, the theoretical relationship between TE and CCM has not been formally explained. Here, we provide a theoretical formulation that links TE and CCM in an information-theoretic framework, showing that they have different definitions of causal influence. Furthermore, from the formulation, we propose a novel nonparametric causality test, named unified information-theoretic causality (UIC), which has lower data requirements than those of TE (due to robustness to noise) and lower false-positive rates than those of CCM. Numerical experiments confirmed that UIC outperforms TE and CCM in terms of the performance of causality detection for both linear and nonlinear dynamical systems. Finally, we demonstrate the practical importance of the conditional test based on UIC using numerical simulations with indirect causality and empirical data for microbial communities in irrigated water samples from rice fields. Our results contribute to a unified understanding of nonparametric causality tests in time series and the accurate reconstruction of complex real-world systems.

ecology↗

Detecting and validating influential organisms for rice growth: An ecological network approach

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.

ecology↗

Facilitative interaction networks in experimental microbial community dynamics

Understanding potential roles of facilitative interactions between species is one of the major challenges in ecology and microbiology. However, we still have limited knowledge of entangled webs of facilitative interactions in ecosystems. By compiling whole-genome shotgun metagenomic data of an experimental microbial community, we tested the hypothesis that architecture 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.

ecology↗

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 show that 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 explained by the magnitude of gene-repertoire overlaps among bacterial species. 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).

microbiology↗

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

For the conservation and community ecology of arthropods and pest controls on terrestrial plants, survey of arthropods is a crucial step. However, efficient surveys are hindered by challenges in collecting small arthropods, and identifying the species. Environmental DNA (eDNA)-based techniques, such as eDNA metabarcording, help overcome these difficulties in aquatic systems. To apply eDNA metabarcording to terrestrial arthropods, we developed a non-destructive eDNA collection method. In this method, termed "plant flow collection," we spray distilled or tap water, or use 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 the filtered water samples and a DNA barcode region, such as cytochrome c oxidase subunit I (COI) gene, of the extracted DNA is amplified and sequenced using a high-throughput sequencer. We identified more than 38 taxonomic groups of arthropods at the family level, of which 7 were visually observed or artificially introduced species, whereas the other 31 groups of arthropods, including 18 species, were not observed in the visual survey. Put together, these results show that the developed method is effective for detecting the presence of arthropods in plants.

ecology↗

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

Microbiome dynamics are both crucial indicators and 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. We here integrate theoretical and empirical bases for 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 then 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. These 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.

ecology↗

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 is 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 two 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 stringatus 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.

ecology↗

An efficient early-pooling protocol for environmental DNA metabarcoding

Environmental DNA (eDNA) metabarcoding, a method that applies high-throughput sequencing and universal primer sets to eDNA analysis, has been a promising approach for efficient, comprehensive biodiversity monitoring. However, significant money-, labor-, and time-costs are still required for performing eDNA metabarcoding. In the present study, we assessed the performance of an "early-pooling" protocol (a protocol based on the 1st PCR indexing) to reduce the experimental costs of the library preparation for eDNA metabarcoding. Specifically, we performed three experiments to test the effects of 1st PCR and 2nd PCR indexing protocols on the community composition revealed by eDNA metabarcoding, of post-1st-PCR exonuclease purification on index-hopping, and of the number of PCR replicates and eDNA template volume on the number of detected OTUs. By analyzing 204 eDNA libraries from 3 natural aquatic ecosystems and 1 mock eDNA sample, we show that (i) the 1st PCR indexing does not cause clear biases in the outcomes of eDNA metabarcoding, (ii) post-1st-PCR exonuclease purification reduces the risk of index-hopping, and (iii) increasing the eDNA template volume can increase the number of detected OTUs and reduce the variations in detected community compositions, as can increasing the number of the 1st PCR replicates. Our results show that an early-pooling protocol with post-1st-PCR exonuclease purification and an increased amount of DNA template will reduce the risk of index-hopping, the costs for consumables and reagents, and the handling time in the library preparation, and that it produces comparable results to a 2nd-PCR-indexing protocol. Therefore, once a target metabarcoding region is determined and a set of indexed-1st-PCR primers is prepared, the early-pooling protocol provides a cost-, labor-, and time-efficient way to process a large number of samples.

ecology↗

Computational capability of ecological dynamics

Ecological dynamics is driven by an ecological network consisting of complex interactions. Information processing capability of artificial networks has been exploited as a computational resource, yet whether an ecological network possesses a computational capability and how we can exploit it remain unclear. Here, we show that ecological dynamics can be exploited as a computational resource. We call this approach "Ecological Reservoir Computing" (ERC) and developed two types of ERC. In silico ERC reconstructs ecological dynamics from empirical time series and uses simulated system responses as reservoir states, which predicts near future of chaotic dynamics and emulates nonlinear dynamics. The real-time ERC uses population dynamics of a unicellular organism, Tetrahymena thermophila. Medium temperature is an input signal and changes in population abundance are reservoir states. Intriguingly, the real-time ERC has necessary conditions for reservoir computing and is able to make near future predictions of model and empirical time series.

ecology↗

Why are flowers ephemeral? 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. Plants rapidly replace flowers with short lifespan, and old flowers senesce. This 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 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 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. Though the short lifespan of flowers has gotten little attention, it might be an essential defense mechanism to cope with antagonistic microbes that rapidly proliferate on the flowers.

ecology↗