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1. Ordinal level phylogenomics of Odonata based on anchored hybrid enrichment

   Newton, Lacie G; Abbott, John C; Bybee, Seth M; Frandsen, Paul B; Goodman, Aaron; Guralnick, Robert; Kalkman, Vincent J; Lontchi, Judicael F; Lupiyanigdyah, Pungki; Sanchez-Herrera, Melissa; et al (June 2023, ICO 2023 program)

   Sparrow, David (Ed.)

   Odonata comprise approximately 6400 species with extensive morphological and ecological diversity, specifically their colour variation, flight behaviour patterns, and breadth of ecological niches. Additionally, their phylogenetic placement within Insecta as descendants of the first winged insects make them ideal candidates for exploring evolutionary forces that have shaped diversity patterns (e.g., diversification rate shifts) as well as character evolution (e.g., flight behaviour, colour). Even though morphological and ecological traits are relatively well known for most of odonate taxa, the lack of well-supported phylogenetic hypothesis across Odonata have limited the capability of evaluating evolutionary phenomena in a comparative context. Previous studies using various taxon sampling schemes and data types (i.e. morphology, targeted locus approaches) to reconstruct odonate relationships failed to resolve several interfamilial relationships, specifically in groups with likely incomplete lineage sorting and/or introgression. Even though a recent study by Bybee et al. (2021) incorporated genomic-scale anchored hybrid enrichment (AHE) data for phylogenetic reconstruction, the relatively limited taxon sampling likely precluded resolution within the problematic groups. Our study, also targeting AHE loci, greatly expand taxon odonate genera, which resulted in 729 newly generated samples in a addition to 142 samples from Bybee et al. (2021) for a total of 831. With around 500 AHE loci, we aim to resolve historically difficult relationships and construct a robust ordinal phylogeny of Odonata, which will be used as the evolutionary framework to clarify taxonomic classifications and test evolutionary hypotheses regarding shifts in flight behaviours, colours, and diversification rates. 

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2. Linking Biodiversity Data Using Evolutionary History

   https://doi.org/10.3897/biss.3.36207  

   McTavish, Emily Jane (June 2019, Biodiversity Information Science and Standards)

   All life on earth is linked by a shared evolutionary history. Even before Darwin developed the theory of evolution, Linnaeus categorized types of organisms based on their shared traits. We now know these traits derived from these species’ shared ancestry. This evolutionary history provides a natural framework to harness the enormous quantities of biological data being generated today. The Open Tree of Life project is a collaboration developing tools to curate and share evolutionary estimates (phylogenies) covering the entire tree of life (Hinchliff et al. 2015, McTavish et al. 2017). The tree is viewable at https://tree.opentreeoflife.org, and the data is all freely available online. The taxon identifiers used in the Open Tree unified taxonomy (Rees and Cranston 2017) are mapped to identifiers across biological informatics databases, including the Global Biodiversity Information Facility (GBIF), NCBI, and others. Linking these identifiers allows researchers to easily unify data from across these different resources (Fig. 1). Leveraging a unified evolutionary framework across the diversity of life provides new avenues for integrative wide scale research. Downstream tools, such as R packages developed by the R OpenSci foundation (rotl, rgbif) (Michonneau et al. 2016, Chamberlain 2017) and others tools (Revell 2012), make accessing and combining this information straightforward for students as well as researchers (e.g. https://mctavishlab.github.io/BIO144/labs/rotl-rgbif.html). Figure 1. Example linking phylogenetic relationships accessed from the Open Tree of Life with specimen location data from Global Biodiversity Information Facility. For example, a recent publication by Santorelli et al. 2018 linked evolutionary information from Open Tree with species locality data gathered from a local field study as well as GBIF species location records to test a river-barrier hypothesis in the Amazon. By combining these data, the authors were able test a widely held biogeographic hypothesis across 1952 species in 14 taxonomic groups, and found that a river that had been postulated to drive endemism, was in fact not a barrier to gene flow. However, data provenance and taxonomic name reconciliation remain key hurdles to applying data from these large digital biodiversity and evolution community resources to answering biological questions. In the Amazonian river analysis, while they leveraged use of GBIF records as a secondary check on their species records, they relied on their an intensive local field study for their major conclusions, and preferred taxon specific phylogenetic resources over Open Tree where they were available (Santorelli et al. 2018). When Li et al. 2018 assessed large scale phylogenetic approaches, including Open Tree, for measuring community diversity, they found that synthesis phylogenies were less resolved than purpose-built phylogenies, but also found that these synthetic phylogenies were sufficient for community level phylogenetic diversity analyses. Nonetheless, data quality concerns have limited adoption of analyses data from centralized resources (McTavish et al. 2017). Taxonomic name recognition and reconciliation across databases also remains a hurdle for large scale analyses, despite several ongoing efforts to improve taxonomic interoperability and unify taxonomies, such at Catalogue of Life + (Bánki et al. 2018). In order to support innovative science, large scale digital data resources need to facilitate data linkage between resources, and address researchers' data quality and provenance concerns. I will present the model that the Open Tree of Life is using to provide evolutionary data at the scale of the entire tree of life, while maintaining traceable provenance to the publications and taxonomies these evolutionary relationships are inferred from. I will discuss the hurdles to adoption of these large scale resources by researchers, as well as the opportunities for new research avenues provided by the connections between evolutionary inferences and biodiversity digital databases. 

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3. Response to “No evidence that transmissible cancer has shifted from emergence to endemism in Tasmanian devils”

   https://doi.org/10.1101/2025.07.14.664746  

   Storfer, Andrew; Margres, Mark J; Kozakiewicz, Christopher P; Hamede, Rodrigo; Ruiz-Aravena, Manuel; Hamilton, David G; Comte, Sebastian; Stadler, Tanja; Leaché, Adam; McCallum, Hamish; et al (July 2025, bioRxiv)

   Abstract Herein, we rebut the critique of Patton et al. (2020), entitled, “No evidence that a transmissible cancer has shifted from emergence to endemism”, by Stammnitz et al. (2024). First and foremost, the authors do not conduct any phylogenetic or epidemiological analyses to rebut the inferences from the main results of the Patton et al. (2020) article, rendering the title of their rebuttal without evidence or merit. Additionally, Stammnitz et al. (2024) present a phylogenetic tree based on only 32 copy number variants (not typically used in phylogenetic analyses and evolve in a completely different way than DNA sequences) to “rebut” our tree that was inferred from 436.1 kb of sequence data and nearly two orders of magnitude more parsimony-informative sites (2520 SNPs). As such it is not surprising that their phylogeny did not have a similar branching pattern to ours, given that support for each branch of their tree was weak and the essentially formed a polytomy. That is, one could rotate their resulting tree in any direction and by nature, it would not match ours. While the authors are correct that we used suboptimal filtering of our raw whole genome sequencing data, re-analyses of the data with 30X coverage, as suggested, resulted in a mutation rate similar to that reported in Stammnitz et al. (2024). Most importantly, when we re-analyzed our data, as well as Stammnitz et al.’s own data, the results of the Patton et al. (2020) article are supported with both datasets. That is, the effective transmission rate of DFTD has transitioned over time to approach one, suggesting endemism; and, the spread of DFTD is rapid and omnidirectional despite the observed east-to-west wave of spread. Overall, Stammnitz et al. (2024) not only fail to provide evidence to contradict the findings of Patton et al. (2020), but rather help support the results with their own data. 

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4. Tempo and mode of gene expression evolution in the brain across primates

   https://doi.org/10.7554/eLife.70276  

   Rickelton, Katherine; Zintel, Trisha M; Pizzollo, Jason; Miller, Emily; Ely, John J; Raghanti, Mary Ann; Hopkins, William D; Hof, Patrick R; Sherwood, Chet C; Bauernfeind, Amy L; et al (January 2024, eLife)

   Primate evolution has led to a remarkable diversity of behavioral specializations and pronounced brain size variation among species (Barton, 2012; DeCasien and Higham, 2019; Powell et al., 2017). Gene expression provides a promising opportunity for studying the molecular basis of brain evolution, but it has been explored in very few primate species to date (e.g. Khaitovich et al., 2005; Khrameeva et al., 2020; Ma et al., 2022; Somel et al., 2009). To understand the landscape of gene expression evolution across the primate lineage, we generated and analyzed RNA-seq data from four brain regions in an unprecedented eighteen species. Here, we show a remarkable level of variation in gene expression among hominid species, including humans and chimpanzees, despite their relatively recent divergence time from other primates. We found that individual genes display a wide range of expression dynamics across evolutionary time reflective of the diverse selection pressures acting on genes within primate brain tissue. Using our samples that represent a 190-fold difference in primate brain size, we identified genes with variation in expression most correlated with brain size. Our study extensively broadens the phylogenetic context of what is known about the molecular evolution of the brain across primates and identifies novel candidate genes for the study of genetic regulation of brain evolution. 

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5. A global biogeographic regionalization for butterflies

   https://doi.org/10.1098/rstb.2023.0211  

   Gross, Collin P; Wright, April M; Daru, Barnabas H (January 2025, Philosophical Transactions of the Royal Society B: Biological Sciences)

   The partitioning of global biodiversity into biogeographic regions is critical for understanding the impacts of global-scale ecological and evolutionary processes on species assemblages as well as prioritizing areas for conservation. However, the lack of globally comprehensive data on species distributions precludes fine-scale estimation of biogeographical regionalization for numerous taxa of ecological, economic and conservation interest. Using a recently published phylogeny and novel curated native range maps for over 10 000 species of butterflies around the world, we delineated biogeographic regions for the world’s butterflies using phylogenetic dissimilarity. We uncovered 19 distinct phylogenetically delimited regions (phyloregions) nested within 6 realms. Regional boundaries were predicted by spatial turnover in modern-day temperature and precipitation seasonality, but historical climate change also left a pronounced fingerprint on deeper- (realm-) level boundaries. We use a culturally and ecologically important group of insects to expand our understanding of how historical and contemporary factors drive the distribution of organismal lineages on the Earth. As insects and global biodiversity more generally face unprecedented challenges from anthropogenic factors, our research provides the groundwork for prioritizing regions and taxa for conservation, especially with the goal of preserving the legacies of our biosphere’s evolutionary history. This article is part of the discussion meeting issue ‘Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future’. 

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