Ben C Stöver

TreeGraph 2: Combining and visualizing evidence from different phylogenetic analyses

BackgroundToday it is common to apply multiple potentially conflicting data sources to a given phylogenetic problem. At the same time, several different inference techniques are routinely employed instead of relying on just one. In view of both trends it is becoming increasingly important to be able to efficiently compare different sets of statistical values supporting (or conflicting with) the nodes of a given tree topology, and merging this into a meaningful representation. A tree editor supporting this should also allow for flexible editing operations and be able to produce ready-to-publish figures.ResultsWe developed TreeGraph 2, a GUI-based graphical editor for phylogenetic trees (available from http://treegraph.bioinfweb.info). It allows automatically combining information from different phylogenetic analyses of a given dataset (or from different subsets of the dataset), and helps to identify and graphically present incongruences. The program features versatile editing and formatting options, such as automatically setting line widths or colors according to the value of any of the unlimited number of variables that can be assigned to each node or branch. These node/branch data can be imported from spread sheets or other trees, be calculated from each other by specified mathematical expressions, filtered, copied from and to other internal variables, be kept invisible or set visible and then be freely formatted (individually or across the whole tree). Beyond typical editing operations such as tree rerooting and ladderizing or moving and collapsing of nodes, whole clades can be copied from other files and be inserted (along with all node/branch data and legends), but can also be manually added and, thus, whole trees can quickly be manually constructed de novo. TreeGraph 2 outputs various graphic formats such as SVG, PDF, or PNG, useful for tree figures in both publications and presentations.ConclusionTreeGraph 2 is a user-friendly, fully documented application to produce ready-to-publish trees. It can display any number of annotations in several ways, and permits easily importing and combining them. Additionally, a great number of editing- and formatting-operations is available.

LeafNet: A computer vision system for automatic plant species identification

Abstract Aims Taxon identification is an important step in many plant ecological studies. Its efficiency and reproducibility might greatly benefit from partly automating this task. Image-based identification systems exist, but mostly rely on hand-crafted algorithms to extract sets of features chosen a priori to identify species of selected taxa. In consequence, such systems are restricted to these taxa and additionally require involving experts that provide taxonomical knowledge for developing such customized systems. The aim of this study was to develop a deep learning system to learn discriminative features from leaf images along with a classifier for species identification of plants. By comparing our results with customized systems like LeafSnap we can show that learning the features by a convolutional neural network (CNN) can provide better feature representation for leaf images compared to hand-crafted features. Methods We developed LeafNet, a CNN-based plant identification system. For evaluation, we utilized the publicly available LeafSnap, Flavia and Foliage datasets. Results Evaluating the recognition accuracies of LeafNet on the LeafSnap, Flavia and Foliage datasets reveals a better performance of LeafNet compared to hand-crafted customized systems. Conclusions Given the overall species diversity of plants, the goal of a complete automatisation of visual plant species identification is unlikely to be met solely by continually gathering assemblies of customized, specialized and hand-crafted (and therefore expensive) identification systems. Deep Learning CNN approaches offer a self-learning state-of-the-art alternative that allows adaption to different taxa just by presenting new training data instead of developing new software systems.

Sample data processing in an additive and reproducible taxonomic workflow by using character data persistently linked to preserved individual specimens

We present the model and implementation of a workflow that blazes a trail in systematic biology for the re-usability of character data (data on any kind of characters of pheno- and genotypes of organisms) and their additivity from specimen to taxon level. We take into account that any taxon characterization is based on a limited set of sampled individuals and characters, and that consequently any new individual and any new character may affect the recognition of biological entities and/or the subsequent delimitation and characterization of a taxon. Taxon concepts thus frequently change during the knowledge generation process in systematic biology. Structured character data are therefore not only needed for the knowledge generation process but also for easily adapting characterizations of taxa. We aim to facilitate the construction and reproducibility of taxon characterizations from structured character data of changing sample sets by establishing a stable and unambiguous association between each sampled individual and the data processed from it. Our workflow implementation uses the European Distributed Institute of Taxonomy Platform, a comprehensive taxonomic data management and publication environment to: (i) establish a reproducible connection between sampled individuals and all samples derived from them; (ii) stably link sample-based character data with the metadata of the respective samples; (iii) record and store structured specimen-based character data in formats allowing data exchange; (iv) reversibly assign sample metadata and character datasets to taxa in an editable classification and display them and (v) organize data exchange via standard exchange formats and enable the link between the character datasets and samples in research collections, ensuring high visibility and instant re-usability of the data. The workflow implemented will contribute to organizing the interface between phylogenetic analysis and revisionary taxonomic or monographic work. Database URL: http://campanula.e-taxonomy.net/

Image-Based Identification of Plant Species Using a Model-Free Approach and Active Learning

Collection and maintenance of biodiversity data is in need for automation. We present first results of an automated and model-free approach to the species identification from herbarium specimens kept in herbaria worldwide. Methodologically, our approach relies on standard methods for the detection and description of so-called interest points and their classification into species-characteristic categories using standard supervised learning tools. To keep the approach model-free on the one hand but also offer opportunities for species identification even in very challenging cases on the other hand, we allow to induce specific knowledge about important visual cues by using concepts of active learning on demand. First encouraging results on selected fern species show recognition accuracies between 94i¾ź% and 100i¾ź%.