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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.]

Biodiversity image repositories are crucial sources of training data for machine learning approaches to biological research. Metadata, specifically metadata about object quality, is putatively an important prerequisite to selecting sample subsets for these experiments. This study demonstrates the importance ofimage quality metadatato a species classification experiment involving a corpus of 1935 fish specimen images which were annotated with 22 metadata quality properties. A small subset of high quality images produced an F1 accuracy of 0.41 compared to 0.35 for a taxonomically matched subset of low quality images when used by a convolutional neural network approach to species identification. Using the full corpus of images revealed that image quality differed between correctly classified and misclassified images. We found the visibility of all anatomical features was the most important quality feature for classification accuracy. We suggest biodiversity image repositories consider adopting a minimal set of image quality metadata to support future machine learning projects.

Species classification is an important task that is the foundation of industrial, commercial, ecological, and scientific applications involving the study of species distributions, dynamics, and evolution.While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes.In this work, we propose a new approach for species classification termed Hierarchy-Guided Neural Network (HGNN), which infuses hierarchical taxonomic information into the neural network’s training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use-case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions.We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied.

The study of how the observable features of organisms, i.e., their phenotypes, result from the complex interplay between genetics, development, and the environment, is central to much research in biology. The varied language used in the description of phenotypes, however, impedes the large scale and interdisciplinary analysis of phenotypes by computational methods. The Phenoscape project (www.phenoscape.org) has developed semantic annotation tools and a gene–phenotype knowledgebase, the Phenoscape KB, that uses machine reasoning to connect evolutionary phenotypes from the comparative literature to mutant phenotypes from model organisms. The semantically annotated data enables the linking of novel species phenotypes with candidate genes that may underlie them. Semantic annotation of evolutionary phenotypes further enables previously difficult or novel analyses of comparative anatomy and evolution. These include generating large, synthetic character matrices of presence/absence phenotypes based on inference, and searching for taxa and genes with similar variation profiles using semantic similarity. Phenoscape is further extending these tools to enable users to automatically generate synthetic supermatrices for diverse character types, and use the domain knowledge encoded in ontologies for evolutionary trait analysis. Curating the annotated phenotypes necessary for this research requires significant human curator effort, although semi-automated natural language processing tools promise to expedite the curation of free text. As semantic tools and methods are developed for the biodiversity sciences, new insights from the increasingly connected stores of interoperable phenotypic and genetic data are anticipated. We thank all participants in the Phenotype Ontology Research Coordination Network (RCN) (NSF 0956049) for their vision, contributions, and commitment to developing shared and interoperable resources. During the course of this work the Phenoscape project has been supported by NSF awards 1062404, 1062542, 0641025, 1661529, and the National Evolutionary Synthesis Center (NSF 0905606 and 0423641). This manuscript is based in part on work done by P.M.M. while serving at the U.S. National Science Foundation. The views expressed in this paper do not necessarily reflect those of the National Science Foundation or the United States 314 Government.

The rich knowledge of morphological variation among organisms reported in the systematic literature has remained in free-text format, impractical for use in large-scale synthetic phylogenetic work. This noncomputable format has also precluded linkage to the large knowledgebase of genomic, genetic, developmental, and phenotype data in model organism databases. We have undertaken an effort to prototype a curated, ontology-based evolutionary morphology database that maps to these genetic databases (http://kb.phenoscape.org) to facilitate investigation into the mechanistic basis and evolution of phenotypic diversity. Among the first requirements in establishing this database was the development of a multispecies anatomy ontology with the goal of capturing anatomical data in a systematic and computable manner. An ontology is a formal representation of a set of concepts with defined relationships between those concepts. Multispecies anatomy ontologies in particular are an efficient way to represent the diversity of morphological structures in a clade of organisms, but they present challenges in their development relative to single-species anatomy ontologies. Here, we describe the Teleost Anatomy Ontology (TAO), a multispecies anatomy ontology for teleost fishes derived from the Zebrafish Anatomical Ontology (ZFA) for the purpose of annotating varying morphological features across species. To facilitate interoperability with other anatomy ontologies, TAO uses the Common Anatomy Reference Ontology as a template for its upper level nodes, and TAO and ZFA are synchronized, with zebrafish terms specified as subtypes of teleost terms. We found that the details of ontology architecture have ramifications for querying, and we present general challenges in developing a multispecies anatomy ontology, including refinement of definitions, taxon-specific relationships among terms, and representation of taxonomically variable developmental pathways.

Megapiranha paranensis from the Upper Miocene of Argentina is described based on a large, partially toothed premaxilla as a new genus and species of serrasalmid fish (pacus and piranhas) and is diagnosed and distinguished from other serrasalmids based on the following unique combination of characters: seven premaxillary teeth with the first four arranged in a shallow, zig-zag row, and third tooth shaped similarly to the fourth and fifth teeth; large, triangular, unicuspid crowns with finely serrated cutting edges. The phylogenetic position of Megapiranha was determined by parsimony analysis of morphological characters. The resulting analysis recovered Megapiranha as sister to the piranha clade (Pygopristis, Pygocentrus, Pristobrycon, Serrasalmus) and is supported by two synapomorphies: (1) teeth triangular in labial view with well-developed cutting edges, and (2) serrations along both sides of tooth cutting edges. The pattern of tooth placement exhibited by the fossil Megapiranha is intermediate between the double-row condition of pacus and the single-row condition of piranhas, and suggests how the double row of teeth may have been rearranged into a single row in the evolution of piranhas.

Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance to mutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes). We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology.

© 2016 The Author(s). Background: The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. Construction and content: Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. Utility and discussion: The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. Conclusions: The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. Fil: Thessen, Anne E.. Ronin Institute for Independent Scholarship; Estados Unidos Fil: Bunker, Daniel E.. New Jersey Institute of Technology; Estados Unidos Fil: Buttigieg, Pier Luigi. Helmholtz-Zentrum für Polar-und Meeresforschung; Alemania Fil: Cooper, Laurel D.. State University of Oregon; Estados Unidos Fil: Dahdul, Wasila M.. University of South Dakota; Estados Unidos Fil: Domisch, Sami. University of Yale; Estados Unidos Fil: Franz, Nico M.. Arizona State University; Estados Unidos Fil: Jaiswal, Pankaj. State University of Oregon; Estados Unidos Fil: Lawrence-Dill, Carolyn J.. University of Iowa; Estados Unidos Fil: Midford, Peter E.. No especifica; Fil: Mungall, Christopher J.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina Fil: Specht, Chelsea D.. University of California at Berkeley; Estados Unidos Fil: Vogt, Lars. Universitat Bonn; Alemania Fil: Vos, Rutger Aldo. Naturalis Biodiversity Center; Países Bajos Fil: Walls, Ramona L.. Arizona State University; Estados Unidos Fil: White, Jeffrey W.. United States Department of Agriculture; Estados Unidos Fil: Zhang, Guanyang. Arizona State University; Estados Unidos Fil: Deans, Andrew R.. State University of Pennsylvania; Estados Unidos Fil: Huala, Eva. Phoenix Bioinformatics; Estados Unidos Fil: Lewis, Suzanna E.. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Mabee, Paula M.. University of South Dakota; Estados Unidos

Effects of variable energy intake on chick growth, organ development, and gut-passage time in seabirds were investigated by raising Elegant Tern (Sterna elegans) chicks in the laboratory on three diets involving a major prey species, northern anchovy (Engraulis mordax), and a minor prey species, topsmelt (Atherinops affinis). Anchovies are ∼25% higher in energy content than topsmelt and represent a more abundant but highly fluctuating prey resource. Chicks were raised from age 15 to age 35 days posthatch on one of three diets (n = 8): (1) high-energy anchovy at the average daily intake rate, (2) low-energy topsmelt at the same intake rate, and (3) high-energy topsmelt (i.e. at a biomass equal in energy content to the anchovy diet). Chicks raised on the high-energy anchovy diet had a significantly faster gain of body mass and wing length. All three groups, however, had attained a statistically similar wing length at 35 days posthatch and did not differ in lean dry mass or pectoral muscle mass, indicating that energy was allocated preferentially toward wing development and flight capacity. Most digestive organs were heavier and gut-passage rates faster for chicks fed topsmelt. Bill dimensions and tarsus length showed little difference among chicks on the three diets. Lipid content of chicks that were fed anchovy was twice that of those fed low-energy topsmelt and one-third higher than that of chicks on the high-energy topsmelt diet. Our results indicate that Elegant Terns have evolved certain capacities for growth and digestion in response to variable prey quality that may increase the probability of chick survival. Nevertheless, if Elegant Tern chicks were switched to a lower quality, primarily topsmelt diet, they would be underweight at fledging with heavier digestive organs, lower lipid reserves, and faster gut-passage times, which would place added provisioning demands on the parents. Under that scenario, the prospects for survival and long-term reproductive success might diminish for such an Elegant Tern colony.

The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project has demonstrated that by annotating characters with community ontology terms, links between novel species anatomy and the genes that may underlie them can be made. But given the enormity of the legacy literature, how can this largely unexploited wealth of descriptive data be rendered amenable to large-scale computation? To identify the bottlenecks, we quantified the time involved in the major aspects of phenotype curation as we annotated characters from the vertebrate phylogenetic systematics literature. This involves attaching fully computable logical expressions consisting of ontology terms to the descriptions in character-by-taxon matrices. The workflow consists of: (i) data preparation, (ii) phenotype annotation, (iii) ontology development and (iv) curation team discussions and software development feedback. Our results showed that the completion of this work required two person-years by a team of two post-docs, a lead data curator, and students. Manual data preparation required close to 13% of the effort. This part in particular could be reduced substantially with better community data practices, such as depositing fully populated matrices in public repositories. Phenotype annotation required ∼40% of the effort. We are working to make this more efficient with Natural Language Processing tools. Ontology development (40%), however, remains a highly manual task requiring domain (anatomical) expertise and use of specialized software. The large overhead required for data preparation and ontology development contributed to a low annotation rate of approximately two characters per hour, compared with 14 characters per hour when activity was restricted to character annotation. Unlocking the potential of the vast stores of morphological descriptions requires better tools for efficiently processing natural language, and better community practices towards a born-digital morphology. Database URL: http://kb.phenoscape.org

Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits., Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Background: Phenex (http://phenex.phenoscape.org/) is a desktop application for semantically annotating the phenotypic character matrix datasets common in evolutionary biology. Since its initial publication, we have added new features that address several major bottlenecks in the efficiency of the phenotype curation process: allowing curators during the data curation phase to provisionally request terms that are not yet available from a relevant ontology; supporting quality control against annotation guidelines to reduce later manual review and revision; and enabling the sharing of files for collaboration among curators. Results: We decoupled data annotation from ontology development by creating an Ontology Request Broker (ORB) within Phenex. Curators can use the ORB to request a provisional term for use in data annotation; the provisional term can be automatically replaced with a permanent identifier once the term is added to an ontology. We added a set of annotation consistency checks to prevent common curation errors, reducing the need for later correction. We facilitated collaborative editing by improving the reliability of Phenex when used with online folder sharing services, via file change monitoring and continual autosave. Conclusions: With the addition of these new features, and in particular the Ontology Request Broker, Phenex users have been able to focus more effectively on data annotation. Phenoscape curators using Phenex have reported a smoother annotation workflow, with much reduced interruptions from ontology maintenance and file management issues.

Background A hierarchical taxonomy of organisms is a prerequisite for semantic integration of biodiversity data. Ideally, there would be a single, expansive, authoritative taxonomy that includes extinct and extant taxa, information on synonyms and common names, and monophyletic supraspecific taxa that reflect our current understanding of phylogenetic relationships. Description As a step towards development of such a resource, and to enable large-scale integration of phenotypic data across vertebrates, we created the Vertebrate Taxonomy Ontology (VTO), a semantically defined taxonomic resource derived from the integration of existing taxonomic compilations, and freely distributed under a Creative Commons Zero (CC0) public domain waiver. The VTO includes both extant and extinct vertebrates and currently contains 106,947 taxonomic terms, 22 taxonomic ranks, 104,736 synonyms, and 162,400 cross-references to other taxonomic resources. Key challenges in constructing the VTO included (1) extracting and merging names, synonyms, and identifiers from heterogeneous sources; (2) structuring hierarchies of terms based on evolutionary relationships and the principle of monophyly; and (3) automating this process as much as possible to accommodate updates in source taxonomies. Conclusions The VTO is the primary source of taxonomic information used by the Phenoscape Knowledgebase (http://phenoscape.org/), which integrates genetic and evolutionary phenotype data across both model and non-model vertebrates. The VTO is useful for inferring phenotypic changes on the vertebrate tree of life, which enables queries for candidate genes for various episodes in vertebrate evolution.

Summary The rich phenotypic diversity that characterizes the vertebrate skeleton results from evolutionary changes in regulation of genes that drive development. Although relatively little is known about the genes that underlie the skeletal variation among fish species, significant knowledge of genetics and development is available for zebrafish. Because developmental processes are highly conserved, this knowledge can be leveraged for understanding the evolution of skeletal diversity. We developed the Phenoscape Knowledgebase (KB; http://kb.phenoscape.org) to yield testable hypotheses of candidate genes involved in skeletal evolution. We developed a community anatomy ontology for fishes and ontology-based methods to represent complex free-text character descriptions of species in a computable format. With these tools, we populated the KB with comparative morphological data from the literature on over 2500 teleost fishes (mainly Ostariophysi) resulting in over 500,000 taxon phenotype annotations. The KB integrates these data with similarly structured phenotype data from zebrafish genes (http://zfin.org). Using ontology-based reasoning, candidate genes can be inferred for the phenotypes that vary across taxa, thereby uniting genetic and phenotypic data to formulate evo-devo hypotheses. The morphological data in the KB can be browsed, sorted, and aggregated in ways that provide unprecedented possibilities for data mining and discovery.

The skeleton is of fundamental importance in research in comparative vertebrate morphology, paleontology, biomechanics, developmental biology, and systematics. Motivated by research questions that require computational access to and comparative reasoning across the diverse skeletal phenotypes of vertebrates, we developed a module of anatomical concepts for the skeletal system, the Vertebrate Skeletal Anatomy Ontology (VSAO), to accommodate and unify the existing skeletal terminologies for the species-specific (mouse, the frog Xenopus, zebrafish) and multispecies (teleost, amphibian) vertebrate anatomy ontologies. Previous differences between these terminologies prevented even simple queries across databases pertaining to vertebrate morphology. This module of upper-level and specific skeletal terms currently includes 223 defined terms and 179 synonyms that integrate skeletal cells, tissues, biological processes, organs (skeletal elements such as bones and cartilages), and subdivisions of the skeletal system. The VSAO is designed to integrate with other ontologies, including the Common Anatomy Reference Ontology (CARO), Gene Ontology (GO), Uberon, and Cell Ontology (CL), and it is freely available to the community to be updated with additional terms required for research. Its structure accommodates anatomical variation among vertebrate species in development, structure, and composition. Annotation of diverse vertebrate phenotypes with this ontology will enable novel inquiries across the full spectrum of phenotypic diversity.

The Teleost Taxonomy Ontology (TTO) is an ontology of taxonomic groups and associated names for fish (not just teleosts). This ontology has served as a source of names and taxonomic structure within the Phenoscape project since early 2008. Although the TTO is based on Eschmeyer's Catalog of Fishes (CoF) and incorporates all valid species and genus names, it is also extended by the curation needs of the Phenoscape project. Names of fossil taxa not included in the CoF as well as references to specimens identi*ed only to genus (e.g., Eigenmannia sp. (Fink and Fink 1981)) are incorporated into the TTO as required by the curation needs of the Phenoscape project. As Phenoscape receives updates to the CoF, a tool called TTOUpdate merges the changes into the TTO.

Background Phenotypic differences among species have long been systematically itemized and described by biologists in the process of investigating phylogenetic relationships and trait evolution. Traditionally, these descriptions have been expressed in natural language within the context of individual journal publications or monographs. As such, this rich store of phenotype data has been largely unavailable for statistical and computational comparisons across studies or integration with other biological knowledge. Methodology/Principal Findings Here we describe Phenex, a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic similarities and differences using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Phenex can be configured to load only those ontologies pertinent to a taxonomic group of interest. The graphical user interface was optimized for evolutionary biologists accustomed to working with lists of taxa, characters, character states, and character-by-taxon matrices. Conclusions/Significance Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology.

The Phenoscape project is developing ontologies and tools to integrate morphological and genomic data to address comparative questions in evolutionary biology. We are currently curating 81 publications describing ~5000 phenotypic characters in 4,000 species of Ostariophysian fishes, and will be making our database of ontology-based annotations concurrently with this meeting via a web-based interface at "http://kb.phenoscape.org":http://kb.phenoscape.org.

Background The wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies. Methodology/Principal Findings We used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators. Conclusions/Significance The challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics.

A revised diagnosis of the pimelodid catfish genus Megalonema is given based on synapomorphic features of the Weberian complex and gas bladder. Megalonema xanthum from the Magdalena River is redescribed. Two new cis-Andean species of Megalonema are described, M. amaxanthum n. sp. from the Amazon River basin, and M. orixanthum n. sp. from the Orinoco River basin. These three species are differentially diagnosed by shape and size of the supraoccipital posterior process, adipose-fin shape, vertebral counts, eye size, premaxillary bone shape and dentition, length of the anal-fin base, width between the posterior nostrils and presence/absence of dentations on the pectoral spine. Eretmomegalonema new subgenus is established for M. xanthum, M. amaxanthum and M. orixanthum and supported by the uniquely synapomorphic paddle-like structure of its pelvic fin and hypertrophied basipterygium. Unambiguous synapomorphies indicate a sister-group relationship between M. amaxanthum and M. orixanthum, with M. xanthum basal to this pair. This topology is congruent with the Neogene origins of separate Magdalena, Amazon and Orinoco basins suggesting vicariant control of diversification of Eretmomegalonema. Uma diagnose do gênero Megalonema é fornecida baseada em caracteres sinapomórficos do aparelho de Weber e da bexiga natatória. Megalonema xanthum do rio Magdalena, é redescrita. Duas novas espécies cis-Andinas de Megalonema são descritas: M. amaxanthum sp. n. da bacia Amazônica, e M. orixanthum da bacia do rio Orinoco. Estas três espécies são diagnosticadas pela forma e tamanho do processo supraoccipital posterior, forma da nadadeira adiposa, contagem do número de vértebras, tamanho do olho, forma do premaxilar e dentição, comprimento da base da nadadeira anal, distância entre as narinas posteriores, e presença/ausência de dentições no espinho da nadadeira peitoral. Eretmomegalonema, novo subgênero, é estabelecido para M. xanthum, M. amaxanthum e M. orixanthum e suportado pelas únicas estruturas sinapomórficas da nadadeira pélvica em forma de remo, e do basipterígio hipertrofiado. Claras sinapomorfias indicam uma relação de grupo irmão entre M. amaxanthum e M. orixanthum, com M. xanthum basal a esse grupo. Esta topologia é congruente com a origem Neogênica das distintas bacias do Magdalena, Amazonas e Orinoco, sugerindo um evento vicariante de diversificação de Eretmomegalonema.