The systematics of Speyeria butterflies has historically been complicated by intraspecific variability that has challenged efforts to delimit species and reconstruct phylogenies. Our study presents a phylogenetic comparison of genomic single nucleotide polymorphisms (SNPs) and mitochondrial COI gene sequences, with comprehensive taxon sampling that includes 15 species and 46 subspecies. Increased sampling of genetic markers and taxa improved the match between genetic clusters, obtained with both phylogenetic and cluster‐based analyses, and species previously detected using morphology, as well as showing two species delimitations that may need revision. We also recovered extensive mitonuclear discordance between genomic SNPs and the COI gene, confirming that mitochondrial DNA does not reliably identify several species at broad geographic scales. Resolution of the relationships of Speyeria species demonstrates the importance of sampling variation across the whole genome, and provides an essential foundation for understanding the evolution of this charismatic clade of North American butterflies.

High-throughput sequencing methods for genotyping genome-wide markers are being rapidly adopted for phylogenetics of nonmodel organisms in conservation and biodiversity studies. However, the reproducibility of SNP genotyping and degree of marker overlap or compatibility between datasets from different methodologies have not been tested in nonmodel systems. Using double-digest restriction site-associated DNA sequencing, we sequenced a common set of 22 specimens from the butterfly genus Speyeria on two different Illumina platforms, using two variations of library preparation. We then used a de novo approach to bioinformatic locus assembly and SNP discovery for subsequent phylogenetic analyses. We found a high rate of locus recovery despite differences in library preparation and sequencing platforms, as well as overall high levels of data compatibility after data processing and filtering. These results provide the first application of NGS methods for phylogenetic reconstruction in Speyeria and support the use and long-term viability of SNP genotyping applications in nonmodel systems.

Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.

Understanding the environmental drivers of demographic rates and population dynamics over space and time is critical for anticipating how species will respond to climate change. While the influence of temporal environmental variation and large environmental gradients are well recognized, less is known about how local topography and landscape position influence demography over small spatial scales. Here, we investigate how local landscape position (north-vs. south-facing aspects) influence the demographic rates and population growth of a common bunchgrass in western North America, bluebunch wheatgrass (Pseudoroegneria spicata), using 6 annual censuses measuring growth, survival, and reproductive output. We found notably lower survival on south-facing slopes, particularly among smaller individuals. In contrast, south-facing slopes maintained comparatively high reproductive output in most years, measured both as spikes per plant and spikelets per spike. When we combined these data in demographic models, we found that lower survival among small individuals and greater reliance on reproduction mean south-facing slopes would generally have to maintain higher recruitment for a stable population. Our results highlight the important influence that landscape position and local topography can have in driving population trends. As conditions warm and dry with climate change (north-faces becoming similar to current south-facing slope conditions), bluebunch wheatgrass may become more reliant on reproduction to maintain viable populations and more sensitive to variability in recruitment.

Heterogeneities in parasite infection among conspecific hosts often manifest as sex- or size-biased infections, which are typically attributed to differential host susceptibility and exposure. Since parasite fitness is often tied to host quality, host preference by parasites is likely to be under strong selection. We test the hypothesis that host preference is sufficient to generate variability in infection rate among conspecifics. Specifically, we ask whether the mite Macrocheles muscaedomesticae is able to discriminate between Drosophila hydei hosts of different sex and size, while explicitly accounting for the potential confounding effects of these two factors. Our results indicate a preference for female hosts, but this preference appears to be driven by size and not sex per se. When differences in body size were controlled for, the sex-biased infection disappeared, while mites presented with the choice of two female flies of disparate sizes were more likely to select the larger host. Across the distribution of fly body weight in this study, mites preferentially attached to flies of intermediate size. This study provides evidence that mite choice for certain host types can play an important role in parasite transmission, even in the absence of differential susceptibility or exposure among hosts.