Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: a Case Study with a Bivalve dataset.

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of two deep learning methods - supervised classification approaches and unsupervised similarity learning - to infer organism relationships from specimen images. As a basis, we assembled an image dataset covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this dataset for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our dataset. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.

Species delimitation 4.0: integrative taxonomy meets artificial intelligence.

Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.

Windthrow causes declines in carbohydrate and phenolic concentrations and increased monoterpene emission in Norway spruce.

With the increasing frequencies of extreme weather events caused by climate change, the risk of forest damage from insect attacks grows. Storms and droughts can damage and weaken trees, reduce tree vigour and defence capacity and thus provide host trees that can be successfully attacked by damaging insects, as often observed in Norway spruce stands attacked by the Eurasian spruce bark beetle Ips typographus. Following storms, partially uprooted trees with grounded crowns suffer reduced water uptake and carbon assimilation, which may lower their vigour and decrease their ability to defend against insect attack. We conducted in situ measurements on windthrown and standing control trees to determine the concentrations of non-structural carbohydrates (NSCs), of phenolic defences and volatile monoterpene emissions. These are the main storage and defence compounds responsible for beetle´s pioneer success and host tree selection. Our results show that while sugar and phenolic concentrations of standing trees remained rather constant over a 4-month period, windthrown trees experienced a decrease of 78% and 37% of sugar and phenolic concentrations, respectively. This strong decline was especially pronounced for fructose (-83%) and glucose (-85%) and for taxifolin (-50.1%). Windthrown trees emitted 25 times greater monoterpene concentrations than standing trees, in particular alpha-pinene (23 times greater), beta-pinene (27 times greater) and 3-carene (90 times greater). We conclude that windthrown trees exhibited reduced resources of anti-herbivore and anti-pathogen defence compounds needed for the response to herbivore attack. The enhanced emission rates of volatile terpenes from windthrown trees may provide olfactory cues during bark beetle early swarming related to altered tree defences. Our results contribute to the knowledge of fallen trees vigour and their defence capacity during the first months after the wind-throw disturbance. Yet, the influence of different emission rates and profiles on bark beetle behaviour and host selection requires further investigation.

WARPP-web application for the research of parasitic plants.

The lifestyle of parasitic plants is associated with peculiar morphological, genetic, and physiological adaptations that existing online plant-specific resources fail to adequately represent. Here, we introduce the Web Application for the Research of Parasitic Plants (WARPP) as an online resource dedicated to advancing research and development of parasitic plant biology. WARPP is a framework to facilitate international efforts by providing a central hub of curated evolutionary, ecological, and genetic data. The first version of WARPP provides a community hub for researchers to test this web application, for which curated data revolving around the economically important Broomrape family (Orobanchaceae) is readily accessible. The initial set of WARPP online tools includes a genome browser that centralizes genomic information for sequenced parasitic plant genomes, an orthogroup summary detailing the presence and absence of orthologous genes in parasites compared with nonparasitic plants, and an ancestral trait explorer showing the evolution of life-history preferences along phylogenies. WARPP represents a project under active development and relies on the scientific community to populate the web app's database and further the development of new analysis tools. The first version of WARPP can be securely accessed at https://parasiticplants.app. The source code is licensed under GNU GPLv2 and is available at https://github.com/wickeLab/WARPP.