Cited by

  1. Dakos, V., & Kéfi, S. (2022). Ecological resilience: what to measure and how. Environmental Research Letters, 17(4), 043003. https://iopscience.iop.org/article/10.1088/1748-9326/ac5767/meta
  2. Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., ... & Toju, H. (2023). Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics. Microbiome, 11(1), 63. https://link.springer.com/article/10.1186/s40168-023-01474-5
  3. Sánchez-Pinillos, M., Dakos, V., & Kéfi, S. (2024). Ecological dynamic regimes: A key concept for assessing ecological resilience. Biological Conservation, 289, 110409. https://www.sciencedirect.com/science/article/pii/S0006320723005104
  4. Masuda, N., Islam, S., Thu Aung, S., & Watanabe, T. (2025). Energy landscape analysis based on the Ising model: Tutorial review. PLOS Complex Systems, 2(5), e0000039. https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000039
  5. Roy, M., Mandal, S., Hens, C., Prasad, A., Kuznetsov, N. V., & Dev Shrimali, M. (2022). Model-free prediction of multistability using echo state network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(10). https://pubs.aip.org/aip/cha/article/32/10/101104/2835813
  6. Krause, A. L., Gaffney, E. A., Jewell, T. J., Klika, V., & Walker, B. J. (2024). Turing instabilities are not enough to ensure pattern formation. Bulletin of mathematical biology, 86(2), 21. https://link.springer.com/article/10.1007/s11538-023-01250-4
  7. Miyamoto, H., & Kikuchi, J. (2023). An evaluation of homeostatic plasticity for ecosystems using an analytical data science approach. Computational and Structural Biotechnology Journal, 21, 869-878. https://www.sciencedirect.com/science/article/pii/S2001037023000016
  8. Miyamoto, H., Asano, F., Ishizawa, K., Suda, W., Miyamoto, H., Tsuji, N., ... & Kikuchi, J. (2022). A potential network structure of symbiotic bacteria involved in carbon and nitrogen metabolism of wood-utilizing insect larvae. Science of the Total Environment, 836, 155520. https://www.sciencedirect.com/science/article/pii/S004896972202616X
  9. Miyamoto, H., Kawachi, N., Kurotani, A., Moriya, S., Suda, W., Suzuki, K., ... & Kikuchi, J. (2023). Computational estimation of sediment symbiotic bacterial structures of seagrasses overgrowing downstream of onshore aquaculture. Environmental Research, 219, 115130. https://www.sciencedirect.com/science/article/pii/S0013935122024574
  10. Yonezawa, S., Haruki, T., Koizumi, K., Taketani, A., Oshima, Y., Oku, M., ... & Saito, S. (2024). Establishing monoclonal gammopathy of undetermined significance as an independent pre-disease state of multiple myeloma using Raman spectroscopy, dynamical network biomarker theory, and energy landscape analysis. International Journal of Molecular Sciences, 25(3), 1570. https://www.mdpi.com/1422-0067/25/3/1570
  11. Fujita, H., Yoshida, S., Suzuki, K., & Toju, H. (2025). Alternative stable states of microbiome structure and soil ecosystem functions. Environmental Microbiome, 20(1), 28. https://link.springer.com/article/10.1186/s40793-025-00688-4
  12. Kadoya, T., Suzuki, K., & Terui, A. (2025). Linking energetic instability to compositional changes in biological communities. Proceedings of the National Academy of Sciences, 122(17), e2422701122. https://www.pnas.org/doi/abs/10.1073/pnas.2422701122
  13. Okada, S., Inabu, Y., Miyamoto, H., Suzuki, K., Kato, T., Kurotani, A., ... & Takahashi, H. (2023). Estimation of silent phenotypes of calf antibiotic dysbiosis. scientific reports, 13(1), 6359. https://www.nature.com/articles/s41598-023-33444-0
  14. Kurotani, A., Miyamoto, H., & Kikuchi, J. (2024). Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings. MethodsX, 12, 102528. https://www.sciencedirect.com/science/article/pii/S2215016123005241
  15. Oldenburg, E., Kronberg, R. M., Metfies, K., Wietz, M., von Appen, W. J., Bienhold, C., ... & Ebenhöh, O. (2024). Beyond blooms: the winter ecosystem reset determines microeukaryotic community dynamics in the Fram Strait. Communications Earth & Environment, 5(1), 643. https://www.nature.com/articles/s43247-024-01782-0
  16. Ross, S. R. J., Suzuki, Y., Kondoh, M., Suzuki, K., Villa Martín, P., & Dornelas, M. (2021). Illuminating the intrinsic and extrinsic drivers of ecological stability across scales. Ecological Research, 36(3), 364-378. https://esj-journals.onlinelibrary.wiley.com/doi/abs/10.1111/1440-1703.12214
  17. Sun, B., Yuan, J., Zhang, X., Ma, X., Hao, Z., Wang, L., ... & Li, L. (2025). Metaproteomics Reveals Community Coalescence Outcomes in Co‐Cultured Human Gut Microbiota. Proteomics, 25(17-18), 6-18. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.70009
  18. Watanabe, T., Inoue, K., Kuniyoshi, Y., Nakajima, K., & Aihara, K. (2025). Comparison of Large Language Model with Aphasia. Advanced Science, 12(22), 2414016. https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/advs.202414016
  19. Ito, R., Oku, M., Kimura, I., Haruki, T., Shikata, M., Teramoto, T., ... & Ueda, K. (2025). Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects. Frontiers in Endocrinology, 16, 1576431. https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1576431/abstract
  20. Inouye, B. D., Brosi, B. J., Le Sage, E. H., & Lerdau, M. T. (2021). Trade-offs among resilience, robustness, stability, and performance and how we might study them. Integrative and Comparative Biology, 61(6), 2180-2189. https://academic.oup.com/icb/article-abstract/61/6/2180/6343046
  21. Zhang, K., & Nakaoka, S. (2024). An energy landscape approach reveals the potential key bacteria contributing to the development of inflammatory bowel disease. Plos one, 19(6), e0302151. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0302151
  22. Shima, H., Sakata, K., & Kikuchi, J. (2023). Prediction of influence transmission by water temperature of fish intramuscular metabolites and intestinal microbiota factor cascade using Bayesian networks. Applied Sciences, 13(5), 3198. https://www.mdpi.com/2076-3417/13/5/3198
  23. Miyamoto, H., Suzuki, K., Moriya, S., Matsuura, M., Tsuji, N., Nakaguma, T., ... & Ohno, H. (2025). Symbiotic causal network of seagrass-bacteria-alga-diatom interactions. arXiv preprint arXiv:2511.13799. https://arxiv.org/abs/2511.13799
  24. Fujita, H., Ushio, M., Suzuki, K., Abe, M. S., Yamamichi, M., Iwayama, K., ... & Toju, H. (2022). Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics. bioRxiv, 2022-08. https://www.biorxiv.org/content/10.1101/2022.08.23.505041.abstract
  25. Hu, Y., Cai, J., Gong, Y., Liu, C., Jiang, X., Tang, X., ... & Gao, G. (2023). The collapse and re-establishment of stability regulate the gradual transition of bacterial communities from macrophytes-to phytoplankton-dominated types in a large eutrophic lake. FEMS Microbiology Ecology, 99(10), fiad074. https://academic.oup.com/femsec/article-abstract/99/10/fiad074/7258627
  26. Haj Ali, S., & Hütt, M. T. (2025). How local spectral gaps regulate the multistability of Turing patterns on graphs. PLOS Complex Systems, 2(4), e0000044. https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000044
  27. Oldenburg, E., Kronberg, R. M., Metfies, K., von Appen, W. J., Wietz, M., Bienhold, C., ... & Ebenhöh, O. (2024). Beyond blooms: A novel time series analysis framework predicts seasonal keystone species and sheds light on Arctic pelagic ecosystem stability. bioRxiv, 2024-03. https://www.biorxiv.org/content/10.1101/2024.03.11.583746.abstract
  28. Hayashi, I., Sánchez-Pinillos, M., & Toju, H. (2025). Stochastic Forces in Microbial Community Assembly: Founding Community Size Governs Divergent Ecological Trajectories. bioRxiv, 2025-08. https://www.biorxiv.org/content/10.1101/2025.08.09.669462.abstract
  29. Toju, H., Noguchi, M., Suzuki, K., & Fujita, H. (2025). Statistical inference of keystone taxa reshaping the assembly rules of forest root microbiomes. https://www.researchsquare.com/article/rs-7962497/latest
  30. Li, L., Sun, B., Yuan, J., Zhang, X., Ma, X., Hao, Z., ... & Zhang, L. (2024). Metaproteomics Reveals Competitive Dynamics in Co-Cultured Human Gut Microbiota. https://www.authorea.com/doi/full/10.22541/au.172734860.03598874
  31. Oldenburg, E. (2025). Exploring Arctic Marine Ecosystems: Analysing Multi-Year Data of Arctic Microbial Marine Communities (Doctoral dissertation, Universitäts-und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf). https://docserv.uni-duesseldorf.de/servlets/DerivateServlet/Derivate-74127
  32. Miyamoto, H., Kawachi, N., Kurotani, A., Moriya, S., Suda, W., Suzuki, K., ... & Kikuchi, J. (2022). Estimation of symbiotic bacterial structure in a sustainable seagrass ecosystem on recycled management. arXiv preprint arXiv:2202.06182. https://arxiv.org/abs/2202.06182
  33. Fujita, H., Yoshida, S., Suzuki, K., & Toju, H. (2022). Alternative stable states in soil microbiomes of agroecosystems. bioRxiv, 2022-08. https://www.biorxiv.org/content/10.1101/2022.08.23.505048.abstract
  34. 張愷揚. (2024). Study on energy landscape approach revealing the potential key bacteria contributing to the development of inflammatory bowel disease (Doctoral dissertation, 北海道大学). https://eprints.lib.hokudai.ac.jp/dspace/handle/2115/93292
  35. Zhang, K., & Nakaoka, S. (2023). A novel Energy Landscape method incorporating the Latent Dirichlet Allocation topic model and the pairwise Maximum Entropy model, revealing the significant contribution of Bactericides to the development of Inflammatory Bowel Disease. bioRxiv, 2023-04. https://www.biorxiv.org/content/10.1101/2023.04.19.537426.abstract
  36. 松田孟留. (2025). 非正規化モデルによる統計的推測—スコアマッチングとノイズ対照推定—. 日本統計学会誌, 54(2), 177-203. https://www.jstage.jst.go.jp/article/jjssj/54/2/54_177/_article/-char/ja/
  37. Pisarchik, A. N., & Hramov, A. E. (2022). Manifestation of Multistability in Different Systems. In Multistability in Physical and Living Systems: Characterization and Applications (pp. 111-165). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-98396-3_3
  38. Miyamoto, H., Kawachi, N., Kurotani, A., Moriya, S., Suda, W., Suzuki, K., ... & Kikuchi, J. A Potential Symbiotic Sediment Bacterial Structure That Contributes to Seagrass Growth Downstream of Onshore Aquaculture. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4234696
  39. 合田幸広, 福田真嗣, 紺野勝弘, & 川原信夫. (2022). 国際共同研究分野 (Doctoral dissertation, University of Toyama). https://toyama.repo.nii.ac.jp/record/19578/files/45_06-12.pdf
  40. 早川芳弘, 合田幸広, 福田真嗣, & 紺野勝弘. (2022). 研究開発部門 国際共同研究分野 (各部門・センターの活動と業績) (Doctoral dissertation, University of Toyama). https://toyama.repo.nii.ac.jp/record/19461/files/48_01-11_Page050to053.pdf

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