@article {Morris740373, author = {Andrew H. Morris and Kyle M. Meyer and Brendan J. M. Bohannan}, title = {Linking microbial communities to ecosystem functions: what we can learn from genotype-phenotype mapping in organisms}, elocation-id = {740373}, year = {2019}, doi = {10.1101/740373}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Microorganisms mediate many important ecosystem functions, yet it remains unclear to what extent microbial diversity or community composition is important for determining the rates of ecosystem-scale functions. This uncertainty limits our ability to predict and manage crucial microbially-mediated processes, such as nutrient loss and greenhouse gas emissions. Our lack of understanding stems from the relatively large diversity of microorganisms, the difficulty in directly identifying functional groups, and our limited ability to manipulate microbial community attributes. For this reason, we propose that integrating traditional biodiversity-ecosystem function research with ideas from genotype-phenotype mapping could provide the new perspective our discipline needs. We identify three insights from genotype-phenotype mapping that could be useful for microbial biodiversity-ecosystem function studies: the concept of {\textquotedblleft}agnostic{\textquotedblright} mapping, the use of more powerful ways to account for multiple comparisons, and the incorporation of covariates into models of ecosystem function. We illustrate the potential for these approaches to elucidate microbial biodiversity-ecosystem function relationships by analyzing a subset of published data measuring methane oxidation rates from incubations of tropical soil. We assert that combining the approaches of traditional biodiversity-ecosystem function research with ideas from genotype-phenotype mapping will not only generate novel hypotheses about how complex microbial communities drive ecosystem function, but also help scientists predict and manage changes to ecosystem functions resulting from human activities.}, URL = {https://www.biorxiv.org/content/early/2019/08/20/740373}, eprint = {https://www.biorxiv.org/content/early/2019/08/20/740373.full.pdf}, journal = {bioRxiv} }