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Merz, E., Saberski, E., Gilarranz, L. J., Isles, P. D. F., Sugihara, G., Berger, C., & Pomati, F. (2023). Disruption of ecological networks in lakes by climate change and nutrient fluctuations. Nature Climate Change, 13(4), 389–396. https://doi.org/10.1038/s41558-023-01615-6

Saberski, E., Park, J., Hill, T., Stabenau, E., & Sugihara, G. (2022). Improved Prediction of Managed Water Flow into Everglades National Park Using Empirical Dynamic Modeling. Journal of Water Resources Planning and Management, 148(12), 10. https://doi.org/10.1061/(asce)wr.1943-5452.0001598

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Park, J., Pao, G. M., Sugihara, G., Stabenau, E., & Lorimer, T. (2022). Empirical mode modeling A data-driven approach to recover and forecast nonlinear dynamics from noisy data. Nonlinear Dynamics, 14. https://doi.org/10.1007/s11071-022-07311-y

Medeiros, L. P., Allesina, S., Dakos, V., Sugihara, G., & Saavedra, S. (2022). Ranking species based on sensitivity to perturbations under non‐equilibrium community dynamics. Ecology Letters, ele.14131. https://doi.org/10.1111/ele.14131

Chen, Z., Xu, M., Gao, B., Sugihara, G., Shen, F., Cai, Y., Li, A., Wu, Q., Yang, L., Yao, Q., Chen, X., Yang, J., Zhou, C., & Li, M. (2022). Causation inference in complicated atmospheric environment. Environmental Pollution, 303, 119057. https://doi.org/10.1016/j.envpol.2022.119057

Park, J., Saberski, E., Stabenau, E., & Sugihara, G. (2021). Dynamics of Florida milk production and total phosphate in Lake Okeechobee. PLOS ONE, 16(8), 10. https://doi.org/10.1371/journal.pone.0248910

Giron-Nava, A., Ezcurra, E., Brias, A., Velarde, E., Deyle, E., Cisneros-Montemayor, A. M., Munch, S. B., Sugihara, G., & Aburto-Oropeza, O. (2021). Environmental variability and fishing effects on the Pacific sardine fisheries in the Gulf of California. Canadian Journal of Fisheries and Aquatic Sciences, 78(5), 623–630. https://doi.org/10.1139/cjfas-2020-0010

Lorimer, T., Goodridge, R., Bock, A. K., Agarwal, V., Saberski, E., Sugihara, G., & Rifkin, S. A. (2021). Tracking changes in behavioural dynamics using prediction error. PLOS ONE, 16(5). https://doi.org/10.1371/journal.pone.0251053

Li, J. J., Zyphur, M. J., Sugihara, G., & Laub, P. J. (2021). Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata. Stata Journal, 21(1), 220–258. https://doi.org/10.1177/1536867x211000030

Natsukawa, H., Deyle, E. R., Pao, G. M., Koyamada, K., & Sugihara, G. (2021). A visual analytics approach for ecosystem dynamics based on empirical dynamic modeling. Ieee Transactions on Visualization and Computer Graphics, 27(2), 506–516. https://doi.org/10.1109/tvcg.2020.3028956

Choi, E. S., Saberski, E., Lorimer, T., Smith, C., Kandage-don, U., Burton, R. S., & Sugihara, G. (2020). The importance of making testable predictions: A cautionary tale. PLOS ONE, 15(12). https://doi.org/10.1371/journal.pone.0236541

Nova, N., Deyle, E. R., Shocket, M. S., MacDonald, A. J., Childs, M. L., Rypdal, M., Sugihara, G., & Mordecai, E. A. (2020). Susceptible host availability modulates climate effects on dengue dynamics. Ecology Letters. https://doi.org/10.1111/ele.13652

Kuriyama, P. T., Sugihara, G., Thompson, A. R., & Semmens, B. X. (2020). Identification of shared spatial dynamics in temperature, salinity, and ichthyoplankton community diversity in the California Current system with empirical dynamic modeling. Frontiers in Marine Science, 7. https://doi.org/10.3389/fmars.2020.557940

Chang, C. W., Ye, H., Miki, T., Deyle, E. R., Souissi, S., Anneville, O., Adrian, R., Chiang, Y. R., Ichise, S., Kumagai, M., Matsuzaki, S. S., Shiah, F. K., Wu, J. T., Hsieh, C. H., & Sugihara, G. (2020). Long-term warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks. Global Change Biology. https://doi.org/10.1111/gcb.15323

Munch, S. B., Brias, A., Sugihara, G., & Rogers, T. L. (2020). Frequently asked questions about nonlinear dynamics and empirical dynamic modelling. ICES Journal of Marine Science, 77(4), 1463–1479. https://doi.org/10.1093/icesjms/fsz209

Giron-Nava, A., Munch, S. B., Johnson, A. F., Deyle, E., James, C. C., Saberski, E., Pao, G. M., Aburto-Oropeza, O., & Sugihara, G. (2020). Circularity in fisheries data weakens real world prediction. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-63773-3

Cenci, S., Medeiros, L. P., Sugihara, G., & Saavedra, S. (2020). Assessing the predictability of nonlinear dynamics under smooth parameter changes. Journal of The Royal Society Interface, 17(162), 20190627. https://doi.org/10.1098/rsif.2019.0627

Lee, S. W., Yon, D. K., James, C. C., Lee, S., Koh, H. Y., Sheen, Y. H., Oh, J. W., Han, M. Y., & Sugihara, G. (2019). Short-term effects of multiple outdoor environmental factors on risk of asthma exacerbations: Age-stratified time-series analysis. Journal of Allergy and Clinical Immunology, 144(6), 1542-+. https://doi.org/10.1016/j.jaci.2019.08.037

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Cenci, S., Sugihara, G., & Saavedra, S. (2019). Regularized S-map for inference and forecasting with noisy ecological time series. Methods in Ecology and Evolution, 10(5), 650–660. https://doi.org/10.1111/2041-210x.13150

Rypdal, M., & Sugihara, G. (2019). Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics. Nature Communications, 10. https://doi.org/10.1038/s41467-019-10099-y2374

Munch, S. B., Giron-Nava, A., & Sugihara, G. (2018). Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish and Fisheries, 19(6), 964–973. https://doi.org/10.1111/faf.12304

Deyle, E., Schueller, A. M., Ye, H., Pao, G. M., & Sugihara, G. (2018). Ecosystem-based forecasts of recruitment in two menhaden species. Fish and Fisheries, 19(5), 769–781. https://doi.org/10.1111/faf.12287

Sugihara, G., Criddle, K. R., McQuown, M., Giron-Nava, A., Deyle, E., James, C., Lee, A., Pao, G., Saberski, E., & Ye, H. (2018). Comprehensive incentives for reducing Chinook salmon bycatch in the Bering Sea walleye Pollock fishery: Individual tradable encounter credits. Regional Studies in Marine Science, 22, 70–81. https://doi.org/10.1016/j.rsma.2018.06.002

Ushio, M., Hsieh, C. H., Masuda, R., Deyle, E. R., Ye, H., Chang, C. W., Sugihara, G., & Kondoh, M. (2018). Fluctuating interaction network and time-varying stability of a natural fish community. Nature, 554(7692), 360-+. https://doi.org/10.1038/nature25504

Arndt, T., Florian, G., Philip, C., Takeshi, M., James, W. M., George, S., Stephen, L. D., & Chih-hao, H. (2017). Infections of Wolbachia may destabilize mosquito population dynamics. Journal of Theoretical Biology, 428(Supplement C), 98–105. https://doi.org/10.1016/j.jtbi.2017.05.016

Giron-Nava, A., James, C. C., Johnson, A. F., Dannecker, D., Kolody, B., Lee, A., Nagarkar, M., Pao, G. M., Ye, H., Johns, D. G., & Sugihara, G. (2017). Quantitative argument for long-term ecological monitoring. Marine Ecology Progress Series, 572, 269–274. https://doi.org/10.3354/meps12149

McGowan, J. A., Deyle, E. R., Ye, H., Carter, M. L., Perretti, C. T., Seger, K. D., de Verneil, A., & Sugihara, G. (2017). Predicting coastal algal blooms in Southern California. Ecology. https://doi.org/10.1002/ecy.1804

Dakos, V., Glaser, S. M., Hsieh, C. H., & Sugihara, G. (2017). Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress. Journal of the Royal Society Interface, 14(128). https://doi.org/10.1098/risf.2016.0845

Sugihara, G. (2017). Niche hierarchy: Structure, organization, and assembly in natural systems. J. Ross Publishing.

Deyle, E. R., Maher, M. C., Hernandez, R. D., Basu, S., & Sugihara, G. (2016). Global environmental drivers of influenza. Proceedings of the National Academy of Sciences of the United States of America, 113(46), 13081–13086. https://doi.org/10.1073/pnas.1607747113

Ye, H., & Sugihara, G. (2016). Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science, 353(6302), 922–925. https://doi.org/10.1126/science.aag0863

Rikkert, M., Dakos, V., Buchman, T. G., de Boer, R., Glass, L., Cramer, A. O. J., Levin, S., van Nes, E., Sugihara, G., Ferrari, M. D., Tolner, E. A., van de Leemput, I., Lagro, J., Melis, R., & Scheffer, M. (2016). Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Critical Care Medicine, 44(3), 601–606. https://doi.org/10.1097/ccm.0000000000001564

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Ye, H., Deyle, E. R., Gilarranz, L. J., & Sugihara, G. (2015). Distinguishing time-delayed causal interactions using convergent cross mapping. Scientific Reports, 5. https://doi.org/10.1038/srep14750

Clark, A. T., Ye, H., Isbell, F., Deyle, E. R., Cowles, J., Tilman, G. D., & Sugihara, G. (2015). Spatial convergent cross mapping to detect causal relationships from short time series. Ecology, 96(5), 1174–1181. https://doi.org/10.1890/14-1479.1.sm

van Nes, E. H., Scheffer, M., Brovkin, V., Lenton, T. M., Ye, H., Deyle, E., & Sugihara, G. (2015). Causal feedbacks in climate change. Nature Climate Change, 5(5), 445–448. https://doi.org/10.1038/nclimate2568

Ye, H., Beamish, R. J., Glaser, S. M., Grant, S. C. H., Hsieh, C. H., Richards, L. J., Schnute, J. T., & Sugihara, G. (2015). Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proceedings of the National Academy of Sciences of the United States of America, 112(13), E1569–E1576. https://doi.org/10.1073/pnas.1417063112

Tsonis, A. A., Deyle, E. R., May, R. M., Sugihara, G., Swanson, K., Verbeten, J. D., & Wang, G. L. (2015). Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature. Proceedings of the National Academy of Sciences of the United States of America, 112(11), 3253–3256. https://doi.org/10.1073/pnas.1420291112

Liu, H., Fogarty, M. J., Hare, J. A., Hsieh, C. H., Glaser, S. M., Ye, H., Deyle, E., & Sugihara, G. (2014). Modeling dynamic interactions and coherence between marine zooplankton and fishes linked to environmental variability. Journal of Marine Systems, 131, 120–129. https://doi.org/10.1016/j.jmarsys.2013.12.003

Glaser, S. M., Ye, H., & Sugihara, G. (2014). A nonlinear, low data requirement model for producing spatially explicit fishery forecasts. Fisheries Oceanography, 23(1), 45–53. https://doi.org/10.1111/fog.12042

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Perretti, C. T., Sugihara, G., & Munch, S. B. (2013). Nonparametric forecasting outperforms parametric methods for a simulated multispecies system. Ecology, 94(4), 794–800. https://doi.org/10.1890/12-0904.1

Glaser, S. M., Fogarty, M. J., Liu, H., Altman, I., Hsieh, C.-H., Kaufman, L., MacCall, A. D., Rosenberg, A. A., Ye, H., & Sugihara, G. (2013). Complex dynamics may limit prediction in marine fisheries. Fish and Fisheries, n/a-n/a. https://doi.org/10.1111/faf.12037

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