Aug. 2, 2005 Researchers have obtained further evidence that one of the oldest biological laws can also be applied to bacteria living in the sump tank reservoirs of machines in an engineering workshop in Oxford, according to a paper published in Environmental Microbiology.
Scientists from the Centre for Ecology & Hydrology (CEH) in Oxford, found that the patterns of abundance and genetic diversity of bacteria living in oil-based metal-cutting fluid reservoirs were similar to those found in higher animal and plant communities. This confirms the ecological law that states that the bigger the area the greater the number of species present.
Scientists previously thought that biodiversity at the microbial level, was fundamentally different to that of larger organisms, such as plants and mammals. This discovery implies that similar processes, which structure the communities of large organisms, also determine those of microbial communities.
Engineering machine metal-cutting fluid reservoirs are used as coolants and lubricants in metal machining processes. The reservoirs of the machines studied, such as lathes and mills, were of increasing size and were said to be analogous to an archipelago of islands.
Microbial communities impact greatly on life on Earth. They can help promote plant growth and protect plants against disease, as well as reducing pollution. The finding that microorganisms follow the same patterns as plants and animals has potential significance in medicine, agriculture and pollution-control. By applying the new insights to the microbial world, researchers may be able to improve the exploitation of vital microbial processes, such as sewage treatment.
Dr Christopher van der Gast, from CEH, lead author on the paper, comments: 'This is an important finding as it will allow us to predict some fundamental diversity patterns of bacterial communities from information that is fairly simple to obtain. Combined with the amazing technological advances made in environmental microbiology, we hope that we will be able to predict how these communities respond to changes in their environment, as well engineering these communities to perform useful tasks.'
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