Does developmental anatomy have a future in the age of molecular biology and digital technologies? Specifically, will morphological characters continue to be used in comparative developmental biology, or will new types of character be defined? Traditionally, comparative embryology was a non-quantitative, 'portrait-gallery' science. Wilhelm His attempted to develop a character-based, more quantitative approach. Quantitative approaches to development have been also been suggested by Meinhardt and others. With the current availability of computing power and the growth of bioinformatics and phylogenetic methodology, quantitative methodologies are increasingly being applied to studies of embryonic development. Our aim in this article is to examine some of these approaches. In both anatomical and molecular studies, the parameters to be quantified are temporal and spatial. Temporal data are analysed by techniques, such as event pairing, that analyse developmental sequences. In this case, the characters are developmental events. Spatial information can be analysed using morphometrics, in combination with computer-assisted 3D reconstruction. In spatial analyses, anatomical parts may be used as the characters. A major challenge in the coming years is to develop techniques for analysing 3D patterns of developmental gene expression and to compare them between species or individuals. Such analyses have to be defined in relation to five dimensions: the 3 orthogonal spatial planes; time; and individuals. The difficulties of such analyses are complicated by problems of homology. Some possible solutions are suggested. For example, it may be possible to use voxels as characters, and to assign to them attributes according to gene expression domains. At first sight, it might seem that traditional morphological characters would no longer be required in comparative embryology. However, we believe that some kind of anatomical framework will always be needed in comparative biology. The interplay between classical morphological characters, gene expression patterns and computing methodologies will be an exciting area for future work.
Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is declining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.
The typical black coloured ebony wood (Diospyros, Ebenaceae) is desired as a commercial timber because of its durable and aesthetic properties. Surprisingly, a comprehensive wood anatomical overview of the genus is lacking, making it impossible to fully grasp the diversity in microscopic anatomy and to distinguish between CITES protected species native to Madagascar and the rest. We present the largest microscopic wood anatomical reference database for ebony woods and reconstruct evolutionary patterns in the microscopic wood anatomy within the family level using an earlier generated molecular phylogeny. Wood samples from 246 Diospyros species are described based on standardised light microscope observations. For the ancestral state reconstruction, we selected eight wood anatomical characters from 88 Ebenaceae species (including 29 Malagasy Diospyros species) that were included in the most recently reconstructed family phylogeny. Within Diospyros, the localisation of prismatic crystals (either in axial parenchyma or in rays) shows the highest phylogenetic value and appears to have a biogeographical signal. The molecular defined subclade Diospyros clade IX can be clearly distinguished from other ebony woods by its storied structure. Across Ebenaceae, Lissocarpa is distinguishable from the remaining genera by the combined presence of scalariform and simple vessel perforation plates, and Royena typically has silica bodies instead of prismatic crystals. The local deposition of prismatic crystals and the presence of storied structure allow identifying ebony wood species at the subgeneric level, but species-level identification is not possible. In an attempt to improve the identification accuracy of the CITES protected Malagasy woods, we applied computer vision algorithms based on microscopic images from our reference database (microscopic slides from ca. 1000 Diospyros specimens) and performed chemical profiling based on DART TOFMS.