Divergent Selection on Dispersal Targets Chemosensory and Neuronal Genes in Tribolium castaneum - Michael D. Pointer; Will J. Nash; Lewis G. Spurgin; Mark McMullan; Simon Butler; David S. Richardson (Molecular Ecology, 2026)
Dispersal is key to the life history, ecology and evolution of many organisms and important in pest invasiveness. However, the genetic architecture underlying variation in dispersal behaviour remains poorly understood outside of a few model species. We investigated the genomic basis of dispersal using artificial selection on replicated lines of Tribolium castaneum , a flour beetle, an emergent model system and an economically important agricultural pest. Combining whole‐genome resequencing with population‐level genotype–phenotype association analysis, we identify nine genomic regions and 42 genes associated with selection on dispersal. Identified candidate genes were significantly enriched for functions related to neuronal structure and function, as well as chemosensory behaviour and mating, suggesting that variation in dispersal is mediated by neural and chemosensory pathways. Our results demonstrate that dispersal propensity has a polygenic basis and support an interaction between dispersal and mating ecology in this system. These findings contribute to a deeper understanding of the genetic mechanisms driving dispersal evolution and its role in shaping eco‐evolutionary dynamics.

‘Building the (Im)perfect Beast’: Strategies for Identifying Appropriate Spatial Stock Assessment Model Complexity From an International, Blinded High‐Resolution Simulation Experiment - Aaron M. Berger; Daniel R. Goethel; Simon D. Hoyle; Patrick Lynch; Caren Barceló; Alistar Dunn; Brian J. Langseth; Carolina Minte‐Vera; Jemery Day; Haikun Xu; Francisco Izquierdo; Dan Fu; et al. (Fish and Fisheries, 2026)
Despite their potential to inform sustainable regional harvest and climate‐resilient fisheries management, spatial stock assessment models remain underused for management advice. To identify barriers that inhibit broader use of these methods, we conducted a blinded international simulation experiment mimicking real‐world stock assessment development when confronting spatial complexity. Seven analyst teams built spatially aggregated and spatially explicit assessment models using data simulated from high‐resolution operating models based on Indian Ocean yellowfin tuna and Ross Sea Antarctic toothfish dynamics. Each team documented how assessment software platform, data analyses, model building approach, and diagnostics influenced model complexity and realism. A consensus emerged on key assessment building approaches: (1) conduct high‐resolution data analyses to identify appropriate spatial structure; (2) start with simplified models and incrementally add complexity; (3) iteratively evaluate diagnostics to determine necessary spatial complexity; and (4) maintain models with different spatial structures to aid interpretation. The experiment also revealed several valuable insights for parameterising assessments, including consideration of data pre‐processing with spatiotemporal models to better inform data‐sparse regions; regression trees to identify fleet and spatial structure; trade‐offs in complexity between productivity and movement dynamics to achieve tractable and stable model structures; and ensemble modelling approaches to address structural uncertainty. Our findings demonstrate that international collaborations and simulation experiments are crucial for addressing challenges in implementing spatial stock assessments and for evaluating whether their added complexity is justified given management objectives. Broader collaborations are encouraged to foster innovation in fisheries management and to help recognise the practical trade‐offs between model parsimony and complexity.

First dinosaur remains from Serbia: Sauropod and theropod material from the uppermost Cretaceous (Maastrichtian) of Osmakovo - Zoran Marković; Miloš Milivojević; Richard J. Butler; Paul M. Barrett; Simon Wills; Andrew A. van de Weerd; Wilma Wessels; Predrag Radović (Cretaceous Research, 2025)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.