Meghan Balk

• Kim, Sora L.; Balk, Meghan; Sibert, Elizabeth C. & Whitenack, Lisa (2025). Diving Deeper: Leveraging the Chondrichthyan Fossil Record to Investigate Environmental, Ecological, and Biological Change. Annual Review of Earth and Planetary Science. ISSN 0084-6597. 53(1), s. 223–250. doi: 10.1146/annurev-earth-040523-010455. Fulltekst i vitenarkiv Vis sammendrag

  The extensive chondrichthyan fossil record spans 400+ million years and has a global distribution. Paleontological studies provide a foundation of description and taxonomy to support deeper forays into ecology and evolution considering geographic, morphologic, and functional changes through time with nonanalog species and climate states. Although chondrichthyan teeth are most studied, analyses of dermal denticle metrics and soft tissue imprints are increasing. Recent methodological advances in morphology and geochemistry are elucidating fine-scale details, whereas large datasets and ecological modeling are broadening taxonomic, temporal, and geographic perspectives. The combination of ecological metrics and modeling with environmental reconstruction and climate simulations is opening new horizons to explore form and function, demographic dynamics, and food web structure in ancient marine ecosystems. Ultimately, the traits and taxa that endured or perished during the many catastrophic upheaval events in Earth's history contribute to conservation paleobiology, which is a much-needed perspective for extant chondrichthyans. ▪ The longevity and abundance of the chondrichthyan fossil record elucidates facets of ecological, evolutionary, and environmental histories. ▪ Though lacking postcranial, mineralized skeletons, dental enameloid and dermal denticles exquisitely preserve morphology and geochemistry. ▪ Technical advances in imaging, geochemistry, and modeling clarify the linkages between form and function with respect to physiology, diet, and environment. ▪ Conservation efforts can benefit from the temporal and spatial perspective of chondrichthyan persistence through past global change events.

• Balk, Meghan A.; Pardi, Melissa I.; Tomé, Catalina P.; Pedersen, Rasmus Ø.; Grady, John M. & Lyons, S. Kathleen [Vis alle 11 forfattere av denne artikkelen] (2025). Most mammals do not wander: few species escape continental endemism. Ecography. ISSN 0906-7590. 2025(10). doi: 10.1002/ecog.07966. Fulltekst i vitenarkiv Vis sammendrag

  Terrestrial mammals are found nearly everywhere on Earth. Yet, most taxa are endemic to a single continent; geological, evolutionary, ecological, or physiological filters constrain geographic distributions. Here, we synthesize data on geography, taxonomy, lineage age, dispersal, body size, and diet for > 4000 terrestrial mammals prior to detectable human‐mediated biodiversity losses and quantify factors correlated with the likelihood of dispersal between continents. We confirm the uniqueness of being on multiple continents: excluding humans and commensals, only 260 mammals are found on two continents, while six span three or more continents (the red deer, red fox, brown bear, least weasel, and common bent‐wing bat), and just a single species – the lion – once had a geographic range that included four continents. Clearly the challenges of colonizing and persisting on multiple continents are severe. No single characteristic enables taxa to be on more than one continent. Rather, a suite of prerequisite conditions under some circumstances lead to distributions spanning multiple continents. The suite of factors facilitating the occupation of two continents, like being volant, are distinct from those that lead to the occupation of three or more, which are primarily faunivores. Other than humans and our commensals, very few species have become truly cosmopolitan over evolutionary time and geographic space.

• Matentzoglu, Nicolas; Bello, Susan M.; Stefancsik, Ray; Alghamdi, Sarah M.; Anagnostopoulos, Anna V. & Balhoff, James P. [Vis alle 46 forfattere av denne artikkelen] (2025). The Unified Phenotype Ontology: a framework for cross-species integrative phenomics. Genetics. ISSN 0016-6731. 229(3). doi: 10.1093/genetics/iyaf027. Fulltekst i vitenarkiv Vis sammendrag

  Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

• Tsuboi, Masahito; Sztepanacz, Jacqueline; Lisle, Stephen De; Voje, Kjetil L.; Grabowski, Mark & Hopkins, Melanie J. [Vis alle 25 forfattere av denne artikkelen] (2024). The paradox of predictability provides a bridge between micro- and macroevolution. Journal of Evolutionary Biology. ISSN 1010-061X. 37(12), s. 1413–1432. doi: 10.1093/jeb/voae103. Fulltekst i vitenarkiv Vis sammendrag

  The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence is often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this “paradox of predictability.” We begin by explaining why the divergence–variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding of how evolution works within and across timescales.

• Duque, J.C. Girón; Mikó, I; Balk, Meghan; Dahdul, W.; Lapp, H. & Alhajjar, E. [Vis alle 18 forfattere av denne artikkelen] (2024). Meeting Report for the Phenoscape TraitFest 2023 with Comments on Organizing Interdisciplinary Meetings. Biodiversity Information Science and Standards (BISS). 8, s. e115232–e115232. doi: 10.3897/biss.8.115232. Fulltekst i vitenarkiv Vis sammendrag

  The Phenoscape project has developed ontology-based tools and a knowledge base that enables the integration and discovery of phenotypes across species from the scientific literature. The Phenoscape TraitFest 2023 event aimed to promote innovative applications that adopt the capabilities supported by the data in the Phenoscape Knowledgebase and its corresponding semantics-enabled tools, algorithms and infrastructure. The event brought together 26 participants, including domain experts in biodiversity informatics, taxonomy and phylogenetics and software developers from various life-sciences programming toolkits and phylogenetic software projects, for an intense four-day collaborative software coding event. The event was designed as a hands-on workshop, based on the Open Space Technology methodology, in which participants self-organise into subgroups to collaboratively plan and work on their shared research interests. We describe how the workshop was organised, the projects developed and outcomes resulting from the workshop, as well as the challenges in bringing together a diverse group of participants to engage productively in a collaborative environment.

• Balk, Meghan; Bradley, J.; Maruf, M.; Altintas, B.; Bakis, Y. & Bart, H.L. [Vis alle 17 forfattere av denne artikkelen] (2024). A FAIR and modular image-based workflow for knowledge discovery in the emerging field of imageomics. Methods in Ecology and Evolution. 15(6), s. 1129–1145. doi: 10.1111/2041-210X.14327. Fulltekst i vitenarkiv Vis sammendrag

  Image-based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum-led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi-step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration. We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components. We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi-automated, contain different components than those presented here, or have parallel components for comparative studies. We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.

• Stefancsik, Ray; Balhoff, James P.; Balk, Meghan; Ball, Robyn L.; Bello, Susan M. & Caron, Anita R. [Vis alle 26 forfattere av denne artikkelen] (2023). The Ontology of Biological Attributes (OBA)—computational traits for the life sciences. Mammalian Genome. ISSN 0938-8990. 34, s. 364–378. doi: 10.1007/s00335-023-09992-1. Fulltekst i vitenarkiv Vis sammendrag

  Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

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• Balk, Meghan & Voje, Kjetil L. (2026). Genetic constraints unaffected by selection predicts evolution within a fossil lineage. Fulltekst i vitenarkiv
• Balk, Meghan (2025). The role of trait variation across scales. Fulltekst i vitenarkiv
• Zanandrea, Irene; Voje, Kjetil L. & Balk, Meghan (2024). Assessing the importance of variational properties and developmental bias in predicting evolution in mammals. Fulltekst i vitenarkiv
• Balk, Meghan (2024). How do animals respond to a changing planet? Linking micro to macro processes to assess species’ ability to persist. Fulltekst i vitenarkiv
• Balk, Meghan & Voje, Kjetil L. (2024). Is phenotypic evolution predicted by directions of high trait variation during anagenesis and cladogenesis? Fulltekst i vitenarkiv
• Balk, Meghan & Voje, Kjetil L. (2024). The role of trait variation in predicting directions phenotypic evolution during anagenesis and cladogenesis. Fulltekst i vitenarkiv
• Balk, Meghan (2023). Predicting the direction of evolution through time. Fulltekst i vitenarkiv
• Balk, Meghan (2023). small data  BIG DATA BIG QUESTIONS  small studies. Fulltekst i vitenarkiv
• Balk, Meghan & Voje, Kjetil Lysne (2023). Assessing evolvability in a fossil lineage using a CV pipeline to produce a phenomic dataset. Fulltekst i vitenarkiv
• Balk, Meghan (2023). CLONAL COLONIAL ORGANISMS AS A MODEL SYSTEM FOR APPLYING EVOLUTIONARY QUANTITATIVE GENETICS TO THE FOSSIL RECORD. Fulltekst i vitenarkiv

Se alle arbeider i NVA