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Plant Genus

The carnivorous plant genus Genlisea A. St.-Hil. (Lentibulariaceae) comprises at least 22 species distributed in South and Central America as well as in Africa (including Madagascar). It has only recently been shown to be a true carnivore, specialized in protozoa and other small soil organisms. Here we present a statistically highly supported phylogeny of Genlisea based on three chloroplast loci. The most recent common ancestor of Genlisea most likely was of Neotropical origin and characterized by pedicels that are recurved in fruit, a strongly glandular inflorescence, and bivalvate capsule dehiscence. The further evolution of various morphological characters during the diversification of the genus is discussed. The two previously suggested subgenera Tayloria and Genlisea correspond to the two major clades found in our analyses. In subgenus Genlisea, three clades can be clearly distinguished based on molecular and morphological characters and on biogeographic patterns, which led us to propose a new sectional classification.

plant genus

Amaranthus species are an emerging and promising nutritious traditional vegetable food source. Morphological plasticity and poorly resolved dendrograms have led to the need for well resolved species phylogenies. We hypothesized that whole chloroplast phylogenomics would result in more reliable differentiation between closely related amaranth species. The aims of the study were therefore: to construct a fully assembled, annotated chloroplast genome sequence of Amaranthus tricolor; to characterize Amaranthus accessions phylogenetically by comparing barcoding genes (matK, rbcL, ITS) with whole chloroplast sequencing; and to use whole chloroplast phylogenomics to resolve deeper phylogenetic relationships. We generated a complete A. tricolor chloroplast sequence of 150,027 bp. The three barcoding genes revealed poor inter- and intra-species resolution with low bootstrap support. Whole chloroplast phylogenomics of 59 Amaranthus accessions increased the number of parsimoniously informative sites from 92 to 481 compared to the barcoding genes, allowing improved separation of amaranth species. Our results support previous findings that two geographically independent domestication events of Amaranthus hybridus likely gave rise to several species within the Hybridus complex, namely Amaranthus dubius, Amaranthus quitensis, Amaranthus caudatus, Amaranthus cruentus and Amaranthus hypochondriacus. Poor resolution of species within the Hybridus complex supports the recent and ongoing domestication within the complex, and highlights the limitation of chloroplast data for resolving recent evolution. The weedy Amaranthus retroflexus and Amaranthus powellii was found to share a common ancestor with the Hybridus complex. Leafy amaranth, Amaranthus tricolor, Amaranthus blitum, Amaranthus viridis and Amaranthus graecizans formed a stable sister lineage to the aforementioned species across the phylogenetic trees. This study demonstrates the power of next-generation sequencing data and reference-based assemblies to resolve phylogenies, and also facilitated the identification of unknown Amaranthus accessions from a local genebank. The informative phylogeny of the Amaranthus genus will aid in selecting accessions for breeding advanced genotypes to satisfy global food demand.

Yellow to red colored betalains are a chemotaxonomic feature of Caryophyllales, while in most other plant taxa, anthocyanins are responsible for these colors. The carnivorous plant family Nepenthaceae belongs to Caryophyllales; here, red-pigmented tissues seem to attract insect prey. Strikingly, the chemical nature of red color in Nepenthes has never been elucidated. Although belonging to Caryophyllales, in Nepenthes, some molecular evidence supports the presence of anthocyanins rather than betalains. However, there was previously no direct chemical proof of this. Using ultra-high-performance liquid chromatography-electrospray ionization-high-resolution mass spectrometry, we identified cyanidin glycosides in Nepenthes species and tissues. Further, we reveal the existence of a complete set of constitutively expressed anthocyanin biosynthetic genes in Nepenthes. Thus, here we finally conclude the long-term open question regarding red pigmentation in Nepenthaceae.

Modern plant taxonomy reflects phylogenetic relationships among taxa based on proposed morphological and genetic similarities. However, taxonomical relation is not necessarily reflected by close overall resemblance, but rather by commonality of very specific morphological characters or similarity on the molecular level. It is an open research question to which extent phylogenetic relations within higher taxonomic levels such as genera and families are reflected by shared visual characters of the constituting species. As a consequence, it is even more questionable whether the taxonomy of plants at these levels can be identified from images using machine learning techniques.

Whereas previous studies on automated plant identification from images focused on the species level, we investigated classification at higher taxonomic levels such as genera and families. We used images of 1000 plant species that are representative for the flora of Western Europe. We tested how accurate a visual representation of genera and families can be learned from images of their species in order to identify the taxonomy of species included in and excluded from learning. Using natural images with random content, roughly 500 images per species are required for accurate classification. The classification accuracy for 1000 species amounts to 82.2% and increases to 85.9% and 88.4% on genus and family level. Classifying species excluded from training, the accuracy significantly reduces to 38.3% and 38.7% on genus and family level. Excluded species of well represented genera and families can be classified with 67.8% and 52.8% accuracy.

Our results show that shared visual characters are indeed present at higher taxonomic levels. Most dominantly they are preserved in flowers and leaves, and enable state-of-the-art classification algorithms to learn accurate visual representations of plant genera and families. Given a sufficient amount and composition of training data, we show that this allows for high classification accuracy increasing with the taxonomic level and even facilitating the taxonomic identification of species excluded from the training process.

Taxonomy is the science of describing, classifying and ordering organisms based on shared biological characteristics [1]. Species form the basic entities in this system and are aggregated to higher categories such as genera, families or orders depending on characteristics that reflect common ancestry. Each category in this system can be referred to as a taxon. Biological systematics uses taxonomy as a tool to reconstruct the evolutionary history of all taxa [2]. Historically, this aggregation was based on the commonality of specific morphological and anatomical characteristics [1, 2]. However, with the availability and inclusion of molecular data [3, 4] the view on phylogenetic relationships has been subject to a number of fundamental changes even on the level of families and orders, compared to the pre-molecular era [5, 6]. The evolutionary relationships underlying the phylogenetic tree which is reflected in current taxonomic system are not necessarily accompanied by apparent morphological relationships and visual resemblance. As a consequence, it is unclear whether images of plants depict visual characters that reflect the phylogenetic commonality of higher taxonomic levels.

Examples of misclassified images. First and third column display the classified images, second and fourth column the predicted class. Red frames indicate wrong genus classification in hierarchy experiments, but correct direct classification at genus level. Orange frames indicate confusion with species of the same genus. Best viewed in electronic form

Class-averaged top-1 classification accuracy vs. number of images representing each species, genus, or family. Solid lines display the average accuracy and filled areas display the corresponding standard deviation

We evaluated the class-averaged classification accuracy with respect to the number of images representing each class (see Fig. 3). While the figure only provides an aggregated view across all genera and all families, the Supporting Information section contains additional tables on the accuracy per taxon. We observed that more images result in a classifier with higher accuracy, a trend similar to that observed for the InS experiments (cp. Fig. 2). However, we also observed a considerably higher variance in the trend. The achieved accuracy is not only influenced by the number of images, but also by the specific genus or family that was classified. Table 3 displays the five genera and families with best and worst classification accuracy.

Given enough training images, the classifier successfully identified genus and family of trained species (InS) but more interestingly also of species excluded from training (ExS). To achieve these results, the classifiers learned distinct visual characters of genera and families. To visualize the reasoning of the classifiers on the test set images, we highlighted the neural attention, i.e., image regions responsible for classification, in terms of heat maps [15]. We manually evaluated several hundred images of genera and families. Representative images of flowers and leaves along with the neural attention at genus and family level are shown in Fig. 4. Most notably, the classifiers do not learn any background information. Instead, neural attention covers relevant plants or parts thereof. We observed that the classifiers often paid attention to characters such as leaf shape, texture, and margins, as well as attachment of the leaf. For some taxa, leaves seemed more relevant to the classifier if compared to flowers (cp. Cornus, Primula, Rhamnus, Fabaceae). For other taxa, flowers and inflorescence seemed more relevant than leaves (cp. Prunella, Salvia, Vinca, Geraniacea, Lamiaceae). Additional images covering more taxa are shown in the Additional file 1. 041b061a72


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