Patterns in soil organic carbon dynamics: integrating microbial activity, chemotaxis and data-driven approaches
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913451831459840 |
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| author | Monti, Angela Diele, Fasma Lacitignola, Deborah Marangi, Carmela |
| author_facet | Monti, Angela Diele, Fasma Lacitignola, Deborah Marangi, Carmela |
| contents | Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewice dynamic mode decomposition (pDMD) to reconstruct them. Our findings show that pDMD is effective at precisely recreating chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_20625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Patterns in soil organic carbon dynamics: integrating microbial activity, chemotaxis and data-driven approaches Monti, Angela Diele, Fasma Lacitignola, Deborah Marangi, Carmela Numerical Analysis Quantitative Methods Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewice dynamic mode decomposition (pDMD) to reconstruct them. Our findings show that pDMD is effective at precisely recreating chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability. |
| title | Patterns in soil organic carbon dynamics: integrating microbial activity, chemotaxis and data-driven approaches |
| topic | Numerical Analysis Quantitative Methods |
| url | https://arxiv.org/abs/2407.20625 |