Automatic extraction of leaf characters from herbarium specimens

The sizes and shapes (physiognomy) of fossil leaves are widely applied as proxies for paleoclimatic and paleoecological variables. However, significant improvements to leaf-margin analysis, used for nearly a century to reconstruct mean annual temperature (MAT), have been elusive; also, relationships between physiognomy and many leaf ecological variables have not been quantified. Using the recently developed technique of digital leaf physiognomy, correlations of leaf physiognomy to MAT, leaf mass per area, and nitrogen content are quantified for a set of test sites from North and Central America. Many physiognomic variables correlate significantly with MAT, indicating a coordinated, convergent evolutionary response of fewer teeth, smaller tooth area, and lower degree of blade dissection in warmer environments. In addition, tooth area correlates negatively with leaf mass per area and positively with nitrogen content. Multiple linear regressions based on a subset of variables produce more accurate MAT estimates than leaf-margin analysis (standard errors of ±2 vs. ±3°C); improvements are greatest at sites with shallow water tables that are analogous to many fossil sites. The multivariate regressions remain robust even when based on one leaf per species, and the model most applicable to fossils shows no more signal degradation from leaf fragmentation than leaf-margin analysis.

Background An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process. Results We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned. Conclusion We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.