LTER Publications - Ji, Z. and F. B. Dazzo. 2015. CMEIAS JFrad: A digital computing tool to discriminate the fractal geometry of landscape architectures and spatial patterns of individual cells in microbial biofilms. Microbial Ecology 69:710-720.

Ji, Z., K. J. Card, and F. B. Dazzo. 2015. CMEIAS JFrad: A digital computing tool to discriminate the fractal geometry of landscape architectures and spatial patterns of individual cells in microbial biofilms. Microbial Ecology 69:710-720.

Citable PDF link: https://lter.kbs.msu.edu/pub/3451

Image analysis of fractal geometry can be used to gain deeper insights into complex ecophysiological patterns and processes occurring within natural microbial biofilmlandscapes, including the scale-dependent heterogeneities of their spatial architecture, biomass, and cell-cell interactions, all driven by the colonization behavior of optimal spatial positioning of organisms to maximize their efficiency in utilization of allocated nutrient resources. Here, we introduce CMEIAS JFrad, a new computing technology that analyzes the fractal geometry of complex biofilm architectures in digital landscape images. The software uniquely features a data-mining opportunity based on a comprehensive collection of 11 different mathematical methods to compute fractal dimension that are implemented into a wizard design tomaximize ease-of-use for semi-automatic analysis of single images or fully automatic analysis of multiple images in a batch process. As examples of application, quantitative analyses of fractal dimension were used to optimize the important variable settings of brightness threshold and minimumobject size in order to discriminate the complex architecture of freshwater microbial biofilms at multiple spatial scales, and also to differentiate the spatial patterns of individual bacterial cells that influence their cooperative interactions, resource use, and apportionment in situ. Version 1.0 of JFrad is implemented into a software package containing the program files, user manual, and tutorial images that will be freely available at http://cme.msu.edu/cmeias/. This improvement in computational image informatics will strengthen microscopy-based approaches to analyze the dynamic landscape ecology of microbial biofilm populations and communities in situ at spatial resolutions that range from single cells to microcolonies.

DOI: 10.1007/s00248-014-0495-1

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