Resource limitation and population fluctuation drive spatiotemporal order in microbial communities.

Microbial communities display complex spatiotemporal behaviors leading to spatially-structured and ordered organization driven by species interactions and environmental factors. Resource availability plays a pivotal role in shaping the dynamics of bacterial colonies. In this study, we delve into the intricate interplay between resource limitation and the emergent properties of a growing colony of two visually distinct bacterial strains having similar growth and mechanical properties. Employing an agent-based modeling and computer simulations, we analyze the resource-driven effect on segregation and sectoring, cell length regulation and nematic ordering within a growing colony. We introduce a dimensionless parameter referred to as the active layer thickness, derived from nutrient diffusion equations, indicating effective population participation due to local resource availability. Our results reveal that lower values of active layer thickness arising from decreased resource abundance lead to rougher colony fronts, fostering heightened population fluctuations within the colony and faster spatial genetic diversity loss. Our temporal analyses unveil the dynamics of mean cell length and fluctuations, showcasing how initial disturbances evolve as colonies are exposed to nutrients and subsequently settle. Furthermore, examining microscopic details, we find that lower resource levels yield diverse cell lengths and enhanced nematic ordering, driven by the increased prevalence of longer rod-shaped cells. Our investigation sheds light on the multifaceted relationship between resource constraints and bacterial colony dynamics, revealing insights into their spatiotemporal organization.

Framework for global sensitivity analysis in a complex 1D-2D coupled hydrodynamic model: Highlighting its importance on flood management over large data-scarce regions.

Sensitivity analysis determines how perturbation or variation in the values of an independent variable affects a particular dependent variable. The present study attempts to comprehend the sensitivity of the static input parameters on the accuracy of the outputs in a hydrodynamic flood model, which subsequently improves the model accuracy. Hydrodynamic flood modeling is computationally strenuous and data-intensive. Moreover, the accuracy of the flood model outputs is extremely sensitive to the quality of hydrologic and hydraulic inputs, along with a set of static parameters that are traditionally assumed and primarily used for calibration. Therefore, we focus on developing a framework for global sensitivity analysis (GSA) of static input parameters in a 1D-2D coupled hydrodynamic flood modeling system. A set of numerical experiments is conducted by perturbing various combinations of input parameters from their standard (or observed) values to generate flow hydrographs. Nonparametric probability density functions (PDFs) of the river discharge at different locations are compared to calculate the Kullback-Leibler (KL) entropy or KL-divergence, which is used to quantify the sensitivity of the input parameters. We demonstrated the proposed framework on a highly flood-prone rural catchment of the Shilabati River in West Bengal, India, and infer that the sensitivity of the static input parameters is highly dynamic, and their importance varies spatially from the upstream to the downstream of the river. However, Manning's n values of the channel and the banks are significantly sensitive irrespective of the location in the river reach. We suggest that any flood modeling exercise should accompany a GSA, which sets a guideline for the modelers to prioritize the set of sensitive static input parameters during data monitoring, collection, and retrieval. This study is the first attempt at a GSA in a 1D-2D coupled hydrodynamic flood modeling system, whose importance cannot be over-emphasized in any flood modeling platform. The proposed novel framework is generic and can be implemented prior to flood risk analyses for any floodplain management exercise. All free and commercially-available flood models can incorporate the proposed framework for a GSA as an extension toolbox.