Koller, Adam L

Abstract Concerns about declining gray fox (Urocyon cinereoargenteus) populations in the Midwest, United States, prompted a petition to list the prairie gray fox subspecies (Urocyon cinereoargenteus ocythous) under the US Endangered Species Act. Although previous studies identified a deep phylogenetic break between eastern and western lineages of the gray fox at the Great Plains Suture Zone, population structure within the Midwest remains poorly understood, complicating subspecies classification. To address this, we genotyped 339 individuals from the eastern lineage of the gray fox using single nucleotide polymorphisms (SNPs) generated from 2bRAD genomic sequencing to assess genetic diversity and structure. Bayesian clustering and principal component analysis supported three genetic clusters (K = 3), revealing a cryptic break within Missouri that subdivides the morphologically defined range of U. c. ocythous and a secondary break coinciding with the Mississippi River and the currently accepted subspecies boundary. However, low pairwise FST values and admixture indicate weak overall genetic differentiation with ongoing gene flow, whereas declining genetic diversity at northern latitudes is reflective of founder effects from post-glacial population expansion. Consistent with prior mitochondrial DNA (mtDNA) results, nuclear SNP data suggest that U. c. ocythous and Urocyon cinereoargenteus cinereoargenteus are unlikely to represent distinct evolutionary units. However, because 2bRAD detects primarily neutral variation, whole-genome sequencing will be needed to test for adaptive divergence that might persist despite gene flow. Our findings provide an important step toward a comprehensive understanding of population structure and refining subspecies boundaries within the eastern lineage of the gray fox.

Abstract Background Upper-airway and respiratory conditions impose a large and growing global burden, yet there is no standardized way to assess whether a nasal microbiome is “healthy”. Currently, monitoring remains reactive, beginning only after symptoms manifest. The nasal cavity is well suited to proactive monitoring: it shapes respiratory health and pathogen colonization resistance, and can be sampled non-invasively and repeatedly. To address this gap, we introduce the Nasal Microbiome Wellness Index (NMWI), a disease-agnostic, continuous score of nasal microbiome health derived from LASSO-penalized logistic regression. Rather than counting taxa, it learns which taxa (and in what balance) characterize a healthy nose and returns the predicted log-odds that a profile resembles a healthy state. The index was trained on 1654 nasal 16S rRNA gene amplicon sequencing samples (589 healthy, 1065 non-healthy) pooled from 27 publicly available studies, uniformly reprocessed through a single computational pipeline. Results The NMWI comprises an interpretable signature of 24 taxa whose combined relative abundances determine the health-associated log-odds. Health-associated genera such as Corynebacterium and Cutibacterium raised the score, while dysbiosis-associated genera such as Pseudomonas and Escherichia – Shigella lowered it. The NMWI substantially outperformed the Shannon, Simpson, and Chao1 diversity indices, which showed negligible, directionally inconsistent separation between healthy and non-healthy samples (|Cliff’s δ | = 0.01–0.20), whereas the NMWI produced large, consistent separation ( δ = 0.65). It achieved a balanced accuracy of 74.37% on the training data (resubstitution estimate), and 73.43% under repeated 10-times 10-fold cross-validation. Performance remained stable at mean balanced accuracy of 73.82% under a leave-one-study-out framework, reflecting cross-study generalizability. In independent external cohorts, the balanced accuracy was 71.49%, and leave-one-disease-out analysis (in which each disease condition was withheld from training) showed a mean balanced accuracy of 63.92% across unseen conditions, consistent with a disease-agnostic design. The index also generalized across heterogeneous datasets spanning multiple 16S rRNA gene hypervariable regions—to our knowledge the first demonstration of such cross-study, cross-region transferability for the nasal cavity. Conclusions The NMWI distills a complex nasal microbial profile into a single interpretable score computed directly from the 16S rRNA gene data that dominate existing nasal research, making it immediately applicable to published and future datasets without re-sequencing. By replacing descriptive, diversity-based comparison with a quantitative standard, it offers a reproducible, open-source foundation for cross-study benchmarking, individual-level phenotyping, and longitudinal respiratory wellness monitoring.