Genome size variation in wild and cultivated maize along altitudinal gradients
Article first published online: 2 APR 2013
© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust
Volume 199, Issue 1, pages 264–276, July 2013
How to Cite
Díez, C. M., Gaut, B. S., Meca, E., Scheinvar, E., Montes-Hernandez, S., Eguiarte, L. E. and Tenaillon, M. I. (2013), Genome size variation in wild and cultivated maize along altitudinal gradients. New Phytologist, 199: 264–276. doi: 10.1111/nph.12247
- Issue published online: 28 MAY 2013
- Article first published online: 2 APR 2013
- Manuscript Accepted: 20 FEB 2013
- Manuscript Received: 28 NOV 2012
- Agence Nationale de la Recherche. Grant Numbers: ANR-12-ADAP-002, P09-AGR-5010
- Consejería Economía, Innovación Ciencia y Empleo de la Junta de Andalucía, Spain. Grant Numbers: #49298, ANR-12-ADAP-002, P09-AGR-5010
- UC-MEXUS. Grant Numbers: #49298, ANR-12-ADAP-002, P09-AGR-5010
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Fig. S1 Spatial autocorrelation statistics for genome size (GS) for the maize landrace and teosinte data.
Fig. S2 Quantile regressions between genome size (GS) and elevation when the teosinte samples are considered by subspecies rather than by gradient.
Fig. S3 Comparison between the observed and predicted genome size (GS) for maize (cultivated) and teosintes (wild), based on the linear partial least-squares regression (PLSR) model fitted without latitudinal and longitudinal information.
Fig. S4 Principal component analysis (PCA) with the teosinte populations labeled by subspecies rather than by their gradient of origin.
Table S1 Measures of genome size in pg/2C for individual populations
Table S2 Nonparametric multivariate analysis of variance (PERMANOVA) for teosinte subpopulations
Table S3 Spearman Rank correlations between genome size (GS) and bioclimatic variables for separate teosinte subspecies
Table S4 Environmental variables and their coefficients in the linear partial least-squares regression (PLSR) model without longitude and latitude included in the model