SavedAdd your journal

Journals / Turkish Journal of Mathematics and Computer Science / 2026 / Volume: 11 Special Issue

Early Warning Signals of Oxygen-Plankton Dynamics: Mathematical Approach

Yadigar Şekerci Fırat(AMASYA ÜNİVERSİTESİ)0000-0001-7545-1824
Pages
—
Publication Date
2019-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this case, extinction takes place after a considerable length of time.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Any significant decrease in net oxygen production by phytoplankton is likely to result in the loss of atmospheric oxygen and the global extinction of living beings owing to more than half of the atmospheric oxygen provided by marine phytoplankton.
📊
Key Findings & Data:The rate of oxygen production is known to depend on water temperature and hence can therefore be affected by global warming.
💡
Conclusion & Impact:In this case, extinction takes place after a considerable length of time.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Global warming · differential equation · extinction

Abstract

Any significant decrease in net oxygen production by phytoplankton is likely to result in the loss of atmospheric oxygen and the global extinction of living beings owing to more than half of the atmospheric oxygen provided by marine phytoplankton. The rate of oxygen production is known to depend on water temperature and hence can therefore be affected by global warming. In this work, it is assumed that oxygen production varies with time under the effect of increasing temperature. This ecological problem is addressed theoretically by a couple of plankton-oxygen dynamics. A nonlinear mathematical model is considered to investigate the effect of temperature on oxygen-plankton dynamics. The model is analysed analytical and numerical ways, based on the behavior and complexity of the system's steady state. From the analysis of the model, it has been observed that as temperature level goes above the critical threshold of oxygen production rate the equilibrium density of plankton population decrease due to a decrease in oxygen concentration. It has also been shown that the system can exhibit sustainable dynamics that can still lead to an environmental disaster, i.e. oxygen depletion and plankton extinction. In this case, extinction takes place after a considerable length of time.

🔖

Cite This Article

Fırat, Y. Ş. (2019). Early Warning Signals of Oxygen-Plankton Dynamics: Mathematical Approach. Turkish Journal of Mathematics and Computer Science, Volume: 11 Special Issue. https://ajindex.com/articles/1324865

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Abbott, M. R., Phytoplankton patchiness: Ecological implications and observation methods, In Patch Dynamics, Springer, \textbf{96}(1993), 37--49.
  2. Allegretto, W., Mocenni, C., Vicino, A., {\em Periodic solutions in modelling lagoon ecological interactions}, Journal of mathematical biology, \textbf{51}(2005), 367--388.
  3. Edwards, A.M., Brindley, J., {\em Zooplankton mortality and the dynamical behaviour of plankton population models}, Bulletin of Mathematical Biology, \textbf{61}(1999), 303--339.
  4. Fasham, M., {\em The statistical and mathematical analysis of plankton patchiness}, Oceanogr. Marine Biology Annual Rev., 16(1978), 43--79.
  5. Guttal, V., Jayaprakash, C., {\em Changing skewness: An early warning signal of regime shifts in ecosystems}, Ecology letters, \textbf{11(5)}(2008), 450-460.
  6. Hull, V., Mocenni, C., Falcucci, M., Marchettini, N., {\em A trophodynamic model for the lagoon of fogliano (italy) with ecological dependent modifying parameters}, Ecological modelling, \textbf{134}(2000), 153--167.
  7. Mackas, D.L., Boyd, C.M., {\em Spectral analysis of zooplankton spatial heterogeneity}, Science, \textbf{204}(1979), 62--64.
  8. Malchow, H., Petrovskii, S.V., Hilker, F.M., {\em Models of spatiotemporal pattern formation in plankton dynamics}, Nova Acta Leopoldina NF, \textbf{88}(2003), 325--340.
  9. Malchow, H., Petrovskii, S.V., Venturino, E., Spatiotemporal patterns in ecology and epidemiology: theory, models, and simulation. Chapman \& Hall/CRC Press London, 2008.
  10. Marchettini, N., Mocenni, C., Vicino, A., {\em Integrating slow and fast dynamics in a shallow water coastal lagoon}, Annali di chimica, \textbf{89}(1999), 505--514.
  11. Medvinsky, A.B., et al., {\em Spatiotemporal complexity of plankton and fish dynamics}, SIAM Review., \textbf{44}(2002), 311--370.
  12. Misra, A., {\em Modeling the depletion of dissolved oxygen in a lake due to submerged macrophytes}, Nonlinear Anal. Model. Cont., \textbf{15}(2010), 185--198.
  13. Okubo, A., Diffusion and Ecological Problems: Mathematical Models, Springer-Verlag Berlin, Vol.10, 1980.
  14. Petrovskii, S.V., Malchow, H., Mathematical Models of Marine Ecosystems. Mathematical Models, In: The Encyclopedia of Life Support Systems (EOLSS), EOLSS Publishers, Oxford UK, 2004.
  15. Scheffer, M., Carpenter, S., Foley, J.A., Folke, C., Walker, B., {\em Catastrophic shifts in ecosystems}, Nature, \textbf{413(6856)}(2001), 591.
  16. Scheffer, M., et.al., {\em Early-warning signals for critical transitions}, Nature, \textbf{461(7260)}(2009), 53.
  17. Sekerci, Y., Petrovskii, S., {\em Mathematical modelling of plankton--oxygen dynamics under the climate change}, Bulletin of Mathematical Biology, \textbf{77(12)}(2015), 2325--2353.