Abstract
Phytoplankton blooms emerge from the interplay between nutrient availability, biomass growth, and inhibitory by-products such as toxins or exudates. Here, we develop a mechanistic nutrient–phytoplankton–by-product model that couples Beddington–DeAngelis nutrient uptake, by-product-mediated inhibition, and nutrient-dependent detoxification. Analytical results demonstrate that the system remains biologically feasible and bounded, and that a threshold condition governs bloom initiation. Linear stability and bifurcation analyses reveal how detoxification delays can trigger oscillatory bloom behaviour. Across ecologically realistic parameter regimes, the system tends to a stable coexistence state—either directly or through damped oscillations—rather than exhibiting repeated bloom–crash cycles. Global sensitivity analysis (PRCC and Sobol indices) highlights by-product production, inhibition strength, detoxification rate, toxin-linked mortality, and saturation effects as dominant regulators of stability and damping time. Introducing an explicit ecological delay exposes a critical threshold at which a Hopf bifurcation arises, converting the stable equilibrium into sustained oscillations. Numerical simulations confirm the transversality condition and indicate a supercritical onset. Collectively, these results provide a quantitative diagnostic for distinguishing transient from sustained bloom oscillations and identify measurable ecological processes—particularly detoxification and delayed feedback—that govern transitions between stable and oscillatory regimes.
Similar content being viewed by others
Seasonal refuge patterns of phytoplankton trigger irregular bloom events in a contaminated environment
Phytoplankton community structuring and succession in a competition-neutral resource landscape
Dynamical complexity of a three species food chain model incorporating delays and toxic habitat
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
References
Reynolds, C. S. The Ecology of Phytoplankton (Cambridge University Press, 2006). https://doi.org/10.1017/CBO9780511542145.
Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65(4), 995–1010. https://doi.org/10.1007/s00248-012-0159-y (2013).
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16(8), 471–483. https://doi.org/10.1038/s41579-018-0040-1 (2018).
Paerl, H. W. & Otten, T. G. Duelling CyanoHABs: Unravelling the environmental drivers controlling dominance. Environ. Microbiol. 22(10), 3817–3828. https://doi.org/10.1111/1462-2920.15125 (2020).
Taranu, Z. E. et al. Widespread increases in cyanobacteria blooms are reshaping lake ecosystems. Proc. R. Soc. B 289, 20221056. https://doi.org/10.1098/rspb.2022.1056 (2022).
Huisman, J. et al. Cyanobacterial blooms: The role of climate change and nutrient loading. Nat. Rev. Microbiol. 18(1), 64–76. https://doi.org/10.1038/s41579-019-0286-0 (2020).
Anderson, D. M. et al. Harmful algal blooms and climate change. Harmful Algae 108, 102105. https://doi.org/10.1016/j.hal.2021.102105 (2021).
Glibert, P. M. Harmful algal blooms: A global perspective. J. Oceanol. Limnol. 38, 1225–1244. https://doi.org/10.1007/s00343-020-00070-z (2020).
Legrand, C., Rengefors, K., Fistarol, G. O. & Graneli, E. Allelopathy in phytoplankton: Biochemical, ecological and evolutionary aspects. Phycologia 42(4), 406–419. https://doi.org/10.2216/i0031-8884-42-4-406.1 (2003).
Graneli, E. & Hansen, P. J. Allelopathy in harmful algae: A mechanism to compete for resources. In Ecology of Harmful Algae (eds Graneli, E. & Turner, J. T.) 189–201 (Springer, 2006).
Graneli, E., Weberg, M. & Salomon, P. S. Harmful algal blooms of allelopathic microalgal species: The role of eutrophication. Harmful Algae 8(1), 94–102. https://doi.org/10.1016/j.hal.2008.08.011 (2008).
Fistarol, G. O., Legrand, C. & Graneli, E. Allelopathic effect on a nutrient-limited phytoplankton species. Mar. Ecol. – Prog. Ser. 255, 115–125. https://doi.org/10.3354/meps255115 (2003).
Tillmann, U. et al. Inhibition of competing phytoplankton by Alexandrium ostenfeldii: A review of allelochemical potency. J. Plankton Res. 29(6), 547–565. https://doi.org/10.1093/plankt/fbm041 (2007).
Dzyubenko, E. V. et al. Mechanisms of allelopathy in marine and freshwater microalgae. Harmful Algae 108, 102099. https://doi.org/10.1016/j.hal.2021.102099 (2021).
Zhang, Y. et al. Dynamics of transparent exopolymer particles in bloom-forming cyanobacteria. Environ. Sci. Technol. 56(4), 2449–2459. https://doi.org/10.1021/acs.est.1c05508 (2022).
Hansell, D. A., & Carlson, C. A. (Eds.). Biogeochemistry of Marine Dissolved Organic Matter (2nd ed.). Academic Press. (2015).
Decho, A. W. & Gutierrez, T. Microbial extracellular polymeric substances (EPSs) in ocean systems. Front. Microbiol. 8, 922. https://doi.org/10.3389/fmicb.2017.00922 (2017).
Verdugo, P. Marine microgels. Annu. Rev. Mar. Sci. 4, 375–400. https://doi.org/10.1146/annurev-marine-120709-142759 (2012).
Cisternas-Novoa, C., Lee, C. & Engel, A. Transparent exopolymer particles (TEP) and Coomassie stainable particles (CSP): A review. Mar. Chem. 175, 4–13. https://doi.org/10.1016/j.marchem.2015.03.009 (2015).
Grossart, H. P. et al. Extracellular polymeric substances in aquatic systems: Role and composition. Nat. Microbiol. 6, 169–181. https://doi.org/10.1038/s41564-020-00807-w (2021).
Park, H.-D. et al. Degradation of microcystin by a new bacterium isolated from a hypertrophic lake. Lett. Appl. Microbiol. 33(3), 247–251. https://doi.org/10.1046/j.1472-765X.2001.00993.x (2001).
Bourne, D. G. et al. Enzymatic pathway for the bacterial degradation of the cyanobacterial cyclic peptide toxin microcystin-LR. Appl. Environ. Microbiol. 62(11), 4086–4094 (1996).
Faassen, E. J. Cyanotoxins: Occurrence, toxicology, and detection. Toxins 14(1), 48. https://doi.org/10.3390/toxins14010048 (2022).
Orellana, M. V. et al. Marine microgels as a source of cloud condensation nuclei in the high Arctic. Proc. Natl. Acad. Sci. 108(33), 13612–13617. https://doi.org/10.1073/pnas.1102457108 (2011).
Gerphagnon, M. et al. Virus-phytoplankton interactions and the control of bloom dynamics. ISME Journal 14, 1431–1442. https://doi.org/10.1038/s41396-020-0624-8 (2020).
Su, X. et al. Functional roles of microbial aggregates in aquatic ecosystems. Trends Microbiol. 29(6), 485–497. https://doi.org/10.1016/j.tim.2020.12.007 (2021).
Beversdorf, L. J. et al. Phytoplankton community shifts under warming conditions explain cyanobacteria expansion. Limnol. Oceanogr. 65(12), 3186–3202. https://doi.org/10.1002/lno.11584 (2020).
Boero, F. et al. Marine plankton trophic interactions and regime shifts. Mar. Ecol. – Prog. Ser. 646, 1–15. https://doi.org/10.3354/meps13380 (2020).
Lin, S. et al. Adaptive strategies of harmful algae under nutrient stress. Nat. Commun. 14, 1254. https://doi.org/10.1038/s41467-023-36912-7 (2023).
Lu, J. et al. Global rise of harmful algal blooms: Drivers, risks, and management strategies. Sci. Total Environ. 909, 168771. https://doi.org/10.1016/j.scitotenv.2023.168771 (2024).
Rosenzweig, M. L. & MacArthur, R. H. Graphical representation and stability conditions of predator-prey interactions. Am. Nat. 97(895), 209–223. https://doi.org/10.1086/282272 (1963).
Holling, C. S. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91(5), 293–320. https://doi.org/10.4039/Ent91293-5 (1959).
Beddington, J. R. Mutual interference between parasites or predators and its effect on searching efficiency. J. Anim. Ecol. 44(1), 331–340. https://doi.org/10.2307/3866 (1975).
DeAngelis, D. L., Goldstein, R. A. & O’Neill, R. V. A model for trophic interaction. Ecology 56(4), 881–892. https://doi.org/10.2307/1936298 (1975).
Smith, H. L. & Waltman, P. The Theory of the Chemostat: Dynamics of Microbial Competition (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511530043.
Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394. https://doi.org/10.1146/annurev.mi.03.100149.002103 (1949).
Droop, M. R. The nutrient status of algal cells in continuous culture. J. Mar. Biol. Assoc. U. K. 54(4), 825–855 (1974).
Strogatz, S. H. Nonlinear Dynamics and Chaos 2nd ed. (CRC Press, 2018). https://doi.org/10.1201/9780429492563.
Guckenheimer, J. & Holmes, P. Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Springer https://doi.org/10.1007/978-1-4612-1140-2 (1983).
Kuznetsov, Y. A. Elements of Applied Bifurcation Theory 3rd ed. (Springer, 2004). https://doi.org/10.1007/978-1-4757-3978-7.
Pareek, S. & Baghel, R. S. A food web exhibiting group defenses in spatiotemporal dynamics. Int. J. Appl. Comput. Math. 11(5), 1–30 (2025) (Springer).
Baghel, R. S. Memory and delay-driven dynamics in a tritrophic food chain model with Allee effect and nonlinear predation, Nonlinear Sci., 100069, https://doi.org/10.1016/j.nls.2025.100069 (2025).
Liao, T. & Yin, H. Joint impact of global warming and Allee effect on the phytoplankton-zooplankton dynamics under the mean-reverting Ornstein-Uhlenbeck process. Eur. Phys. J. Plus 140(5), 423 (2025).
Liao, T. The impact of temperature variation on the algae-zooplankton dynamics with size-selective disturbance. Chaos, Solitons & Fractals 181, 114615 (2024).
Liao, T. Dynamical complexity driven by water temperature in a size-dependent phytoplankton-zooplankton model with environmental variability. Chin. J. Phys. 88, 557–583 (2024).
Liao, T. Dynamics complexity induced by salinity in a stochastic phytoplankton-zooplankton model under acid-base changes. Math. Methods Appl. Sci.. 48(12), 12258–12281 (2025).
Li, X. et al. Bifurcation analysis of a new aquatic ecological model with aggregation effect. Math. Comput. Simul. 190, 75–96 (2021).
Saito, M. A. et al. Microbial nutrient limitation in the ocean. Annu. Rev. Mar. Sci. 12, 187–217. https://doi.org/10.1146/annurev-marine-010318-095241 (2020).
Pettersson, L. H. et al. Hybrid models of plankton dynamics: Integrating mechanistic and data-driven frameworks. Ecol. Model. 464, 109844. https://doi.org/10.1016/j.ecolmodel.2022.109844 (2022).
Levine, N. M. et al. Modeling phytoplankton-nutrient interactions under climate change. PNAS 118(30), e2026815118. https://doi.org/10.1073/pnas.2026815118 (2021).
Dakos, V. et al. Indicators of tipping points in ecological systems. Nat. Ecol. Evol. 5, 620–632. https://doi.org/10.1038/s41559-020-01313-9 (2021).
Pareek, S., & Baghel, R. S. Controlling cyanobacterial blooms using a biological filter-feeding and aggregation effect. Iran. J. Sci. pp. 1–12, (Springer, 2025)
Schindler, D. W. Eutrophication and recovery in experimental lakes: Implications for lake management. Science 184(4139), 897–899. https://doi.org/10.1126/science.184.4139.897 (1974).
Carpenter, S. R. Phosphorus control is critical to mitigating eutrophication. Proc. Natl. Acad. Sci. 105(32), 11039–11040. https://doi.org/10.1073/pnas.0806112105 (2008).
Vollenweider, R. A. Scientific Fundamentals of the Eutrophication of Lakes and Flowing Waters. OECD. (1968).
Smith, V. H. & Schindler, D. W. Eutrophication science: Where do we go from here-. Trends Ecol. Evol. 24(4), 201–207. https://doi.org/10.1016/j.tree.2008.11.009 (2009).
Hansell, D. A. & Carlson, C. A. Marine dissolved organic matter and the carbon cycle. Oceanography 14(4), 41–49. https://doi.org/10.5670/oceanog.2001.05 (2001).
Li, M. et al. Global trends in cyanobacterial harmful algal blooms. Nat. Commun. 12, 5859. https://doi.org/10.1038/s41467-021-26188-0 (2021).
Baghel, R. S. A toxins role in controlling chaos with a spatial effect in an aquatic systems. Int. J. Appl. Comput. Math. 11(3), 99 (2025) (Springer).
Baghel, R. S. Spatiotemporal dynamics of toxin producing phytoplankton-zooplankton interactions with Holling Type II functional responses. Results Control Optim. 17, 100478 (2024) (Elsevier).
Skalski, G. T. & Gilliam, J. F. Functional responses with predator interference: Viable alternatives to the Holling type II model. Ecology 82(11), 3083–3092 (2001).
Hairer, E., Norsett, S. P. & Wanner, G. Solving Ordinary Differential Equations I: Nonstiff Problems 2nd ed. (Springer, 2008). https://doi.org/10.1007/978-3-540-78862-1.
Butcher, J. C. Numerical Methods for Ordinary Differential Equations 3rd ed. (Wiley, 2016). https://doi.org/10.1002/9781119121534.
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing 3rd ed. (Cambridge University Press, 2007).
Funding
Open access funding provided by Manipal Academy of Higher Education, Manipal. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Contributions
R.S.B. and S.V. conceptualized and designed the study. R.S.B. conducted the experimental work and data collection. S.V. performed the data analysis and interpretation. N.K. contributed to reviewing, editing, and redrafting the manuscript and handled the correspondence. All authors reviewed and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Declaration on the use of AI
The authors declare that Artificial Intelligence (AI) tools, including QuillBot, were used for paraphrasing and redrafting portions of the manuscript with the aim of improving clarity and readability. All AI-generated content was critically reviewed, verified, and edited by the authors to ensure accuracy, originality, and conformity with academic standards. The authors take full responsibility for the integrity and reliability of the final manuscript.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
About this article
Cite this article
Baghel, R.S., Verma, S. & Khatri, N. Delayed dynamics and detoxification in nutrient-phytoplankto-by-product systems: mechanisms driving bloom stability and oscillations.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32146-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-025-32146-z
Keywords
- Phytoplankton-nutrient dynamics
- Beddington-DeAngelis uptake
- By-product interference (allelopathy)
- Stability and Hopf bifurcation
- Global sensitivity analysis (Sobol, PRCC)
- Delay
Source: Ecology - nature.com
