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DNA sequences were obtained from 32 blade-formingUlvaspecimens collected in 2018 and 2019 from four islands in the Galápagos Archipelago: Fernandina, Floreana, Isabela and San Cristóbal. The loci sequenced were nuclear encoded ITS and plastid encodedrbcL andtufA, all recognized as barcode markers for green algae. Four species were found,Ulva adhaerens,U. lactuca,U. ohnoiandU. tanneri, all of which have had their type specimens sequenced, ensuring the correct application of these names. Only one of these,U. lactuca, was reported historically from the archipelago.Ulva adhaerenswas the species most commonly collected and widely distributed, occurring on all four islands. Previously known only from Japan and Korea, this is the first report ofU. adhaerensfrom the southeast Pacific Ocean.Ulva ohnoiwas collected on three islands, Isabela, Floreana, and San Cristóbal, andU. lactucaonly on the last two.Ulva tanneriis a diminutive, 1–2 cm tall, high intertidal species that is easily overlooked, but likely far more common than the one specimen that was collected. This study of blade-formingUlvaspecies confirms that a concerted effort, using DNA sequencing, is needed to document the seaweed flora of the Galápagos Archipelago.

 

Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data.

In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction withrphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes.

We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets.

Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing ‘phenomics’ revolution. Our new package offers key advancements towards this goal.

 

Abstract There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for the skeletal elements of vertebrate fins and limbs from the Phenoscape Knowledgebase. Using seven competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms (REA) homology model and the Ancestral Value Axioms (AVA) homology model. The AVA model returned all user-expected results in addition to the search term and any of its subclasses. The AVA model also returns any superclass of the query term in which a homology relationship has been asserted. The REA model returned the user-expected results for five out of seven queries. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery. [Homology; ontology; anatomy; morphology; evolution; knowledgebase; phenoscape.] 

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent “parts”, but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies—structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here, we assess whether evolutionary patterns can explain the proximity of ontology-annotated characters within an ontology. To do so, we measure phylogenetic information across characters and evaluate if it matches the hierarchical structure given by ontological knowledge—in much the same way as across-species diversity structure is given by phylogeny. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to data sets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially explained by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher-level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that phylogenetic information does match ontology structure for some anatomical entities, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological data sets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may play a role in explaining it: phylogeny, development, or convergence. [Apidae; Bayesian phylogenetic information; Ostariophysi; Phenoscape; phylogenetic dissonance; semantic similarity.]