Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations
Keywords:
Logistic equations, fungal decomposition, fungal growth, mathematical modelAbstract
As the function of the decomposition of fungi has been clearly researched in the global carbon cycle, it is obviously of value to explore the decomposition rate of fungal populations. This study analyzed the relationship between environmental factors and biodiversity step by step. In order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time, the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited resources. The changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the model. To illustrate the applicability of the model, Laetiporus conifericola and Hyphoderma setigerum were applied as examples. The results showed that the higher the degree of population diversity, the greater the decomposition rate, and the higher the decomposition efficiency of the ecosystem. Its rich species diversity is conducive to accelerating the decomposition of litter, lignocellulose, and the circulation of the entire ecosystem. Based on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10°C, 16°C, and 22°C under standardized laboratory conditions, the growth patterns of the five fungi combinations were simulated. The results revealed a general increase in growth rate with increasing temperature, which verifies the accuracy of the model. Moreover, it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single action. Based on the above model, predictions can be made for fungal growth in different environments, and suitable environments for fungal growth can be determined. In the future, the model can be further optimized, and lignin and cellulose decomposition factors can be added to fit the decomposition of logs. The application scenarios of the model can be further broadened, which can contribute to the restoration and management of the ecological environment, as well as produce good effects in the fields of fungi assisting the global carbon cycle and soil problem restoration. Keywords: Logistic equations, fungal decomposition, fungal growth, mathematical model DOI: 10.25165/j.ijabe.20231601.7405 Citation: Pan J Y, Le W X, Wang Z A, Chen J. Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations. Int J Agric & Biol Eng, 2023; 16(1): 60–65.References
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[13] McGuire K L, Treseder K K. Microbial communities and their relevance for ecosystem models: Decomposition as a case study. Soil Biology and Biochemistry, 2010; 42(4): 529-535.
[14] Walse C, Berg B, Sverdrup H. Review and synthesis of experimental data on organic matter decomposition with respect to the effect of temperature, moisture, and acidity. Environmental Reviews, 1998; 6(1): 25–40.
[15] Boddy L. Fungal community ecology and wood decomposition processes in angiosperms: From standing tree to complete decay of coarse woody debris. Ecological Bulletins, 2001; 49: 43–56.
[16] Landwehr N, Hall M, Frank E. Logistic model trees. Machine Learning, 2005; 59: 161–205.
[17] Lotka A J. Elements of physical biology. Science Progress in the Twentieth Century, 1926; 21(82): 341-343.
[18] Volterra V. Variations and fluctuations of the number of individuals in animal species living together. ICES Journal of Marine Science 1928; 3(1): 3–51.
[19] Zhu C, Yin G. On competitive Lotka-Volterra model in random environments. Journal of Mathematical Analysis and Applications, 2009; 357(1): 154–170.
[20] Liu C Y, Liu Z R, Chen W F. Analysis of interaction between fungi and environment based on Lotka-Volterra model. IOP Conference Series: Earth and Environmental Science, 2021; 804(4): 042035. doi: 10.1088/1755-1315/804/4/042035.
[21] Yadav V, Malanson G. Progress in soil organic matter research: litter decomposition, modelling, monitoring and sequestration. Progress in Physical Geography: Earth and Environment, 2007; 31(2): 131–154.
[22] Lustenhouwer N, Maynard D S, Bradford M A, Lindner D L, Crowther T W. A trait-based understanding of wood decomposition by fungi. Proceedings of the National Academy of Sciences, 2020; 117(21): 11551–11558.
[23] Walker M D, Ingersoll R C, Webber P J. Effects of interannual climate variation on phenology and growth of two alpines forbs. Ecological Society of America, 1995; 76(4): 1067–1083.
[24] Stuart Chapin III F, Johnson D A, McKendrick J D. Seasonal movement of nutrients in plants of differing growth form in an Alaskan tundra ecosystem: Implications for herbivory. The Journal of Ecology, 1980; 68(1): 189–209.
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Published
2023-03-13
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Pan, J., Le, W., Wang, Z., & Chen, J. (2023). Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations. International Journal of Agricultural and Biological Engineering, 16(1), 60–65. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7405
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