Ormandaki Bilgi Sinyalleri: Çevresel Bağlamda Saha Modelleme Yaklaşımı

Year 2026, Volume: 8 Issue: 2, 1 - 18, 15.01.2026

Sergey Borisenok

https://doi.org/10.53508/ijiam.1779879

Abstract

Orman ekosistemlerindeki çeşitli iletişim kanallarındaki bilgi aktarım süreçlerini tanımlamak için basitleştirilmiş ancak etkili bir matematiksel model geliştiriyoruz. Bunlara mantar miselyum tabanlı ağlar, bitki biyoelektrik alanları ve biyotik pompa mekanizması da dahildir. Ormanı dağıtılmış bir bilgi sistemi olarak inceleyerek, bu kanallardaki iletişim sinyallerinin ağlardaki ayrı elemanlar yerine sürekli alanlar olarak yayılması için difüzyon tipi kısmi diferansiyel denklemler sunuyoruz. Her iki durum için de, 1B (düzlemsel cephe dalgası) ve 2B (radyal simetrik dalgalar) için çözümlerin temel analizini yaparak, her kanalda sinyal yayılımının mekansal aralığı ile sinyal hızı arasında bir rekabet olduğunu gösteriyoruz. Kanal, ister kısa bir menzil için yüksek bozunma ile hızlı bilgi sinyali hızı sağlasın (veya bu bozunmayı aşmak için yeterli enerji tüketsin) ister uzun menzilli sinyalleri yeterince düşük bir hızla sağlasın, bir uzlaşmaya varmalıdır. Farklı özelliklere sahip birkaç kanalın varlığının, ormanın bilgi aktarımı için gerekli dengeyi sağlamasını sağladığını düşünüyoruz.
Makale 12. Uluslararası Temel ve Uygulamalı Bilimler Kongresi 2025’te (ICFAS2025) sunulmuştur.

Keywords

Karmaşık rekabet dinamikleri , Yayılma tipi bilgi süreçleri , Mantar miselyum tabanlı ağ , Biyoelektrik alanlar , Biyotik pompa mekanizması

Ethical Statement

Yok

Supporting Institution

Abdullah Gül Üniversitesi

Project Number

Yok

Thanks

Yok

Information Signals in the Forest: Field Modeling Approach in the Environmental Context

Abstract

We develop a simplified yet efficient mathematical model to describe the processes of information transfer in various communication channels within forest ecosystems, including fungal mycelia-based networks, plant bioelectric fields, and the biotic pump mechanism. Studying the forest as a distributed informational system, we present diffusion-type partial differential equations for the spread of communication signals in these channels as continuous fields, rather than discrete elements in networks. Making the basic analysis of the solutions in the form of traveling waves for 1D and 2D, we demonstrate that in each channel, there is a competition between the spatial range of the signal spread and the speed of the signals: the fastest informational signal demonstrates the highest rate of spatial decay. This correlation reflects the fundamental necessity of having several channels for transmitting and processing information in distributed systems, such as a forest, with the absence of a single center for monitoring information processes. Each channel must compromise, whether it serves for fast informational signal speed for a short range with the high decay or provides the long-range signals, but with a sufficiently lower speed. The study of multiple competing information channels corresponding to different time scales of the ecosystem evolution emphasizes the need for an integrated approach to forest studies, taking into account the diverse contributions of various ecosystem components.

Keywords

Complex Dynamics , Diffusion-type Information Processes , Fungal Mycelia-based Network , Bioelectric , Biotic Pump Mechanism

Ethical Statement

No

Supporting Institution

Abdullah Gül University

Project Number

Yok

Thanks

No

References

• Abramowitz, M., & Stegun, I. (1972). Handbook of mathematical functions (10th ed.). Dover.
• Al-Nasser, M., Al-Mansour, Y., & Al-Sayid, N. (2024). The role of mycorrhizal fungi in forest ecosystem health. Journal of Selvicoltura Asean, 1(6), 271–281.
• Armada-Moreira, A., Dar, A. M., Zhao, Z., Cea, C., Gelinas, J., Berggren, M., Costa, A., Khodagholy, D., & Stavrinidou, E. (2023). Plant electrophysiology with conformable organic electronics: Deciphering the propagation of Venus flytrap action potentials. Science Advances, 9, eadh4443. https://doi.org/10.1126/sciadv.adh4443
• Barbosa-Caro, J. C., & Wudick, M. M. (2024). Revisiting plant electric signaling: Challenging an old phenomenon with novel discoveries. Current Opinion in Plant Biology, 79, 102528.
• Bock, B. M., Hoeksema, J. D., Johnson, N. C., & Gehring, C. A. (2025). Evidence for common fungal networks among plants formed by a Dark Septate Endophyte in Sorghum bicolor. Communications Biology, 8, 996.
• Boswell, G. P., & Hopkins, S. (2012). Mycelial response to spatiotemporal nutrient heterogeneity: A velocity-jump mathematical model. Fungal Ecology, 5(2), 124–136.
• Bunyard, P. (2020). Winds and rain: The role of the biotic pump. International Journal of Biosensors and Bioelectronics, 6(5), 113–115.
• Buffi, M., Kelliher, J. M., Robinson, A. J., Gonzalez, D., Cailleau, G., Macalindong, J. A., … Bindschedler, S. (2025). Electrical signaling in fungi: Past and present challenges. FEMS Microbiology Reviews, 49, fuaf009.
• Cantin, G., & Verdiere, N. (2020). Mathematical modeling of complex forest ecosystems: Impacts of deforestation. HAL preprint. https://hal.science/hal-02496187v1/file/forest-ecosystems.pdf
• Cantin, G., Ducrot, A., & Funatsu, B. M. (2021). Mathematical modeling of forest ecosystems by a reaction-diffusion-advection system: Impacts of climate change and deforestation. Journal of Mathematical Biology, 83, 66.
• Castro-Delgado, A. L., Elizondo-Mesén, S., Valladares-Cruz, Y., & Rivera Méndez, W. (2020). Wood Wide Web: Communication through the mycorrhizal network. Tecnología en Marcha, 33(4), 114–125.
• Chiolerio, A., Gagliano, M., Pilia, S., Pilia, P., Vitiello, G., & Dehshibi, M., … Adamatzky, A. (2025). Bioelectrical synchronization of Picea abies during a solar eclipse. Royal Society Open Science, 12, 241786.
• Cui, Z., Zhang, Y., Wang, A., & Wu, J. (2024). Forest evapotranspiration trends and their driving factors under climate change. Journal of Hydrology, 644, 132114.
• Csikós, S., Ádám, B., & Sárosi, J. (2024). Modelling hysteresis with memristors. Analecta Technica Szegedinensia, 18(4), 14–20.
• Davidson, F. A. (2007). Mathematical modelling of mycelia: A question of scale. Fungal Biology Reviews, 21(1), 30–41.
• De Loof, A. (2016). The cell's self-generated 'electrom': The biophysical essence of the immaterial dimension of life? Communicative and Integrative Biology, 9(5), e1197446.
• Dighton, J. (2003). Fungi in ecosystem processes. Marcel Dekker, Inc.
• Figueiredo, A. F., Boy, J., & Guggenberger, G. (2021). Common mycorrhizae network: A review of the theories and mechanisms behind underground interactions. Frontiers in Fungal Biology, 2, 735299.
• Fricker, M., Boddy, L., & Bebber, D. (2007). Network organisation of mycelial fungi. In The Mycota VIII: Biology of the Fungal Cell (pp. 309–330). Springer.
• Fricker, M. D., Lee, J. A., Bebber, D. P., Tlalka, M., Hynes, J., Darrah, P. R., Watkinson, S. C., & Boddy, L. (2008). Imaging complex nutrient dynamics in mycelial networks. Journal of Microscopy, 231(2), 317–331.
• Frew, A., Varga, S., & Klein, T. (2025). Mycorrhizal networks: Understanding hidden complexity. Functional Ecology, 39(6), 1322–1327.
• Fukasawa, Y., Hamano, K., Kaga, K., Akai, D., & Takehi, T. (2024). Spatial resource arrangement influences both network structures and activity of fungal mycelia: A form of pattern recognition? Fungal Ecology, 72, 101387.
• Gil, A., Segura, J., & Temme, N. M. (2015). Computing the Kummer function U(a,b,z) for small values of the arguments. arXiv preprint. https://arxiv.org/abs/1509.05167
• Hassan, M. S. (2024). Mathematical model of fungal growth with hyphae death and substrate. Wasit Journal for Pure Sciences, 3(3), 42–49.
• Hassan, M. S., & Shuaa, A. H. (2024). Mathematical model of fungal growth with substrate. IET Conference Proceedings, 2024(34), 0124.
• Heaton, L., Obara, B., Grau, V., Jones, N., Nakagaki, T., Boddy, L., & Fricker, M. D. (2012). Analysis of fungal networks. Fungal Biology Reviews, 26, 12–29.
• Herceg, D., & Herceg, D. (2020). Improved accuracy hysteresis model based on hypergeometric functions. AIP Advances, 10, 105321.
• Herceg, D., Chwastek, K., & Herceg, Dj. (2020). The use of hypergeometric functions in hysteresis modeling. Energies, 13, 6500.
• Herschtal, A. (2024). Efficient and precise calculation of the confluent hypergeometric function. arXiv preprint. https://arxiv.org/abs/2407.03336
• Huber, K. T., Moulton, V., & Scholz, G. E. (2022). Forest-based networks. Bulletin of Mathematical Biology, 84, 119.
• Kilic, A. B., & Akan, O. B. (2025). Information and communication theoretical foundations of the Internet of plants, principles, challenges, and future directions. arXiv preprint. https://arxiv.org/abs/2509.08434
• Köstner, B. (2001). Evaporation and transpiration from forests in Central Europe – relevance of patch-level studies for spatial scaling. Meteorology and Atmospheric Physics, 76, 69–82.
• Kozlova, E., Yudina, L., Sukhova, E., & Sukhov, V. (2025). Analysis of electrome as a tool for plant monitoring: Progress and perspectives. Plants, 14, 1500.
• Makarieva, A. M., & Gorshkov, V. G. (2007). Biotic pump of atmospheric moisture as driver of the hydrological cycle on land. Hydrology and Earth System Sciences, 11, 1013–1033.
• Makarieva, A. M., & Gorshkov, V. G. (2008). The forest biotic pump of river basins. Russian Journal of Ecology, 39(7), 537–540.
• Makarieva, A. M., & Gorshkov, V. G. (2011). The biotic pump: Condensation, atmospheric dynamics and climate. International Journal of Water, 5(4), 365–385.
• Makarieva, A. M., Gorshkov, V. G., & Li, B.-L. (2013). Revisiting forest impact on atmospheric water vapor transport and precipitation. Theoretical and Applied Climatology, 111, 79–96.
• Mathews Jr., W. N., Esrick, M. A., Teoh, Z. Y., & Freericks, J. K. (2022). A physicist's guide to the solution of Kummer's equation and confluent hypergeometric functions. Condensed Matter Physics, 25(3), 33203.