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Testing the functional significance of microbial composition in natural communities
Author(s) -
Reed Heather E.,
Martiny Jennifer B. H.
Publication year - 2007
Publication title -
fems microbiology ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.377
H-Index - 155
eISSN - 1574-6941
pISSN - 0168-6496
DOI - 10.1111/j.1574-6941.2007.00386.x
Subject(s) - ecosystem , biology , ecology , composition (language) , microbial population biology , reciprocal , microbial ecology , philosophy , linguistics , genetics , bacteria
Ecologists have long studied the relationship between biotic composition and ecosystem functioning in larger organisms; however, only recently has this relationship been investigated widely in microorganisms. Recent studies are reviewed within a framework of three experimental approaches that are often used to study larger organisms: environmental treatment, common garden, and reciprocal transplant experiments. Although the composition of microorganisms cannot be easily manipulated in the field, applying these approaches to intact microbial communities can begin to tease apart the effects of microbial composition from environmental parameters on ecosystem functioning. The challenges in applying these approaches to microorganisms are highlighted and it is discussed how the experimental approach and duration affects a study's interpretation. In general, long‐term environmental treatment experiments identify correlative relationships between microbial composition and ecosystem functioning, whereas short‐term common garden experiments demonstrate that microbial composition influences ecosystem functioning. Finally, reciprocal transplants simultaneously test for interactive effects of the environment and composition on functioning. The studies reviewed provide evidence that, at least in some cases, microbial composition influences ecosystem functioning. It is concluded that whole‐community experiments offer a way to test whether information about microbial composition will help predict ecosystem responses to global change.

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