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Glover JD, Reganold JP, Bell LW, Borevitz J, Brummer EC, Buckler ES, Cox CM, Cox TS, Crews TE, Culman SW, Dehaan LR, Eriksson D, Gill BS, Holland J, Hu F, Hulke BS, Ibrahim AMH, Jackson W, Jones SS, Murray SC, Paterson AH, Ploschuk E, Sacks EJ, Snapp S, Tao D, Van Tassel DL, Wade LJ, Wyse DL, Xu Y. Perennial Questions of Hydrology and Climate—Response. Science 2010. [DOI: 10.1126/science.330.6000.33-b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
| | - J. P. Reganold
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
| | - L. W. Bell
- Sustainable Ecosystems-Agricultural Production Systems Research Unit, Commonwealth Scientific and Industrial Research Organization (Australia), Toowoomba, QLD 4350, Australia
| | - J. Borevitz
- Department of Evolution and Ecology, University of Chicago, Chicago, IL 60637, USA
| | - E. C. Brummer
- Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, Athens, GA 30602, USA
| | - E. S. Buckler
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) and Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - C. M. Cox
- The Land Institute, Salina, KS 67401, USA
| | - T. S. Cox
- The Land Institute, Salina, KS 67401, USA
| | - T. E. Crews
- Environmental Studies, Prescott College, Prescott, AZ 86301, USA
| | - S. W. Culman
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA
| | | | - D. Eriksson
- Department of Plant Breeding and Biotechnology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - B. S. Gill
- Wheat Genetic and Genomic Resources Center, Kansas State University, Manhattan, KS 66506, USA
| | - J. Holland
- USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695, USA
| | - F. Hu
- Food Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - B. S. Hulke
- USDA-ARS Sunflower Research Unit, Northern Crop Science Laboratory, Fargo, ND 58105, USA
| | - A. M. H. Ibrahim
- Department of Soil and Crop Sciences, Texas A & M University, College Station, TX 77843, USA
| | - W. Jackson
- The Land Institute, Salina, KS 67401, USA
| | - S. S. Jones
- Department of Crop and Soil Sciences, Washington State University, Mount Vernon, WA 98273, USA
| | - S. C. Murray
- Department of Soil and Crop Sciences, Texas A & M University, College Station, TX 77843, USA
| | - A. H. Paterson
- Plant Genome Mapping Laboratory, University of Georgia, Athens, GA 30602, USA
| | - E. Ploschuk
- Cátedra de Cultivos Industriales, Facultad de Agronomía, Universidad de Buenos Aires, C1417DSE Buenos Aires, Argentina
| | - E. J. Sacks
- Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA
| | - S. Snapp
- Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA
| | - D. Tao
- Food Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | | | - L. J. Wade
- Charles Sturt University, E. H. Graham Centre for Agricultural Innovation, Wagga Wagga, NSW 2678, Australia
| | - D. L. Wyse
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Y. Xu
- Global Maize Program, International Maize and Wheat Improvement Center, Apartado 0660, Mexico D.F., Mexico
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Glover JD, Reganold JP, Bell LW, Borevitz J, Brummer EC, Buckler ES, Cox CM, Cox TS, Crews TE, Culman SW, DeHaan LR, Eriksson D, Gill BS, Holland J, Hu F, Hulke BS, Ibrahim AMH, Jackson W, Jones SS, Murray SC, Paterson AH, Ploschuk E, Sacks EJ, Snapp S, Tao D, Van Tassel DL, Wade LJ, Wyse DL, Xu Y. Increased Food and Ecosystem Security via Perennial Grains. Science 2010; 328:1638-9. [DOI: 10.1126/science.1188761] [Citation(s) in RCA: 312] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Culman SW, Gauch HG, Blackwood CB, Thies JE. Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study. J Microbiol Methods 2008; 75:55-63. [PMID: 18584903 DOI: 10.1016/j.mimet.2008.04.011] [Citation(s) in RCA: 113] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2008] [Revised: 04/23/2008] [Accepted: 04/29/2008] [Indexed: 10/22/2022]
Abstract
The analysis of T-RFLP data has developed considerably over the last decade, but there remains a lack of consensus about which statistical analyses offer the best means for finding trends in these data. In this study, we empirically tested and theoretically compared ten diverse T-RFLP datasets derived from soil microbial communities using the more common ordination methods in the literature: principal component analysis (PCA), nonmetric multidimensional scaling (NMS) with Sørensen, Jaccard and Euclidean distance measures, correspondence analysis (CA), detrended correspondence analysis (DCA) and a technique new to T-RFLP data analysis, the Additive Main Effects and Multiplicative Interaction (AMMI) model. Our objectives were i) to determine the distribution of variation in T-RFLP datasets using analysis of variance (ANOVA), ii) to determine the more robust and informative multivariate ordination methods for analyzing T-RFLP data, and iii) to compare the methods based on theoretical considerations. For the 10 datasets examined in this study, ANOVA revealed that the variation from Environment main effects was always small, variation from T-RFs main effects was large, and variation from T-RFxEnvironment (TxE) interactions was intermediate. Larger variation due to TxE indicated larger differences in microbial communities between environments/treatments and thus demonstrated the utility of ANOVA to provide an objective assessment of community dissimilarity. The comparison of statistical methods typically yielded similar empirical results. AMMI, T-RF-centered PCA, and DCA were the most robust methods in terms of producing ordinations that consistently reached a consensus with other methods. In datasets with high sample heterogeneity, NMS analyses with Sørensen and Jaccard distance were the most sensitive for recovery of complex gradients. The theoretical comparison showed that some methods hold distinct advantages for T-RFLP analysis, such as estimations of variation captured, realistic or minimal assumptions about the data, reduced weight placed on rare T-RFs, and uniqueness of solutions. Our results lead us to recommend that method selection be guided by T-RFLP dataset complexity and the outlined theoretical criteria. Finally, we recommend using binary or relativized peak height data with soil-based T-RFLP data for ordination-based exploratory microbial analyses.
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Affiliation(s)
- S W Culman
- Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, United States.
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