This research has been supported by the NASA CERES project. (The CERES EBAF Ed4.0 dataset was downloaded from -tool.larc.nasa.gov/ord-tool/jsp/EBAF4Selection.jsp.) The NASA Langley Atmospheric Sciences Data Center processed the instantaneous Single Scanner Footprint (SSF) data used as input to EBAF Ed4.0. Some of the material in this paper is reproduced from the CERES Data Quality Summaries for Edition 2.8 and Edition 4.0 (available online at _summaries/CERES_EBAF_Ed2.8_DQS.pdf and _summaries/CERES_EBAF_Ed4.0_DQS.pdf). The authors thank the editor, Dr. Karen Shell, and three anonymous reviewers for their helpful comments and suggestions.
Most of these phenotypic studies have been possible thanks to the change from field descriptions to quantitative science , due to advances in technologies and statistics that allow morphological data to have greater complexity, comparing parameters among study groups and controls and establishing relations between them, which makes it possible to explain the patterns found [1,21,22]. Comparative anatomy originally used linear variables such as measurements, distances, angles and proportions, which were analyzed with multivariate statistics and expressed as a set of coefficients and graphs. Sometimes, it was difficult to interpret variations in size and shape, given the high correlations of linear variables with size and, although a method (allometry) was devised to remove the size effect, the disparity of the results in traditional morphology (TM) was not satisfactory. Geometric morphometrics (GM) arose due to the limitations of TM. GM allows analyzing the shape of organisms and/or structures using the geometric space and multivariate statistical methods that have better biological interpretation . GM is based on digitizing X and Y coordinates (and Z, in 3-D morphometrics) of the positions of landmarks [24,25,26]; the generalized Procrustes analysis (GPA)  eliminates variations due to scale, rotation and translation. The results are then analyzed with multivariate statistics (e.g., ANOVA, regression) [28,29], which also provide graphic analyses that allow for quantifying and visually understanding the morphometric variation within and among populations [22,30]. Cadrin  suggested that morphometric analysis provides a unique perspective for the study of population structure. In recent decades GM has been fundamental in the study of populations, especially in fish; some studies have used morphometric analysis to identify fish stocks [5,32]; Alarcón-Durán  studied six isolated populations of silverside (Chirostoma humboldtianum), reporting that the habitat and feeding habits influence shape significantly in the different populations. Narváez et al. , analyzed the variation in shape of two populations of domestic and naturalized Oreochromis niloticus, finding morphological disparity due to adaptations to the habitat. 2b1af7f3a8