A University of Oklahoma team of microbiologists have developed a mathematical framework for quantitatively assessing ecological diversity in an ecological community whether deterministic or stochastic. A recent study by the team published in the Proceedings of the National Academy of Sciences examines the mechanisms controlling biological diversity and provides guidance for use of the null-model-based approaches for examining processes within the community.
"An ecological community is a dynamic complex system with a myriad of interacting species. Both deterministic or stochastic forces can shape the community, but how to quantify their relative contribution remains a great challenge. This study provides an effective and robust tool to ecologists for quantitatively assessing ecological stochasticity," said Jizhong Zhou, director of the Institute for Environmental Genomics, professor in the OU Colleges of Arts and Sciences and Gallogy College of Engineering, and affiliate of the U.S. Department of Energy's Lawrence Berkeley National Laboratory.
Zhou led the study with OU team members Daliang Ning and Ye Deng; and James M. Tiedje, Michigan State University. In this study, the team modified the framework for more general situations when quantifying stochastic mechanisms underlying ecological communities and demonstrated that it has obviously better quantitative performance than previous methods.
The team used the framework to reassess the importance of determinism and stochasticity in mediating the succession of groundwater microbial communities in response to organic carbon injection, in this case emulsified vegetable oil, to stimulate bioremediation. Also, the team evaluated the effects of different null-model algorithms and similarity metrics on the quantitative assessment of stochasticity in groundwater microbial communities in response to the carbon injection.
The study results show the microbial community shifted from deterministic to more stochastic right after organic carbon input. As the vegetable oil was consumed, the community returned to more deterministic. In addition, the study results demonstrated that null-model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.
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