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Gene flow between diverging lineages challenges the resolution of species boundaries and the understanding of evolutionary history in recent radiations. Here, we integrate phylogenetic and coalescent tools to resolve reticulate patterns of diversification and use a perspective focused on evolutionary mechanisms to distinguish interspecific and intraspecific taxonomic variation. We use this approach to resolve the systematics for one of the most intensively studied but difficult to understand groups of reptiles: the spotted whiptail lizards of the genus Aspidoscelis (A. gularis complex). Whiptails contain the largest number of unisexual species known within any vertebrate group and the spotted whiptail complex has played a key role in the generation of this diversity through hybrid speciation. Understanding lineage boundaries and the evolutionary history of divergence and reticulation within this group is therefore key to understanding the generation of unisexual diversity in whiptails. Despite this importance, long-standing confusion about their systematics has impeded understanding of which gonochoristic species have contributed to the formation of unisexual lineages. Using reduced representation genomic data, we resolve patterns of divergence and gene flow within the spotted whiptails and clarify patterns of hybrid speciation. We find evidence that biogeographically structured ecological and environmental variation has been important in morphological and genetic diversification, as well as the maintenance of species boundaries in this system. Our study elucidates how gene flow among lineages and the continuous nature of speciation can bias the practice of species delimitation and lead taxonomists operating under different frameworks to different conclusions (here we propose that a 2 species arrangement best reflects our current understanding). In doing so, this study provides conceptual and methodological insights into approaches to resolving diversification patterns and species boundaries in rapid radiations with complex histories, as well as long-standing taxonomic challenges in the field of systematic biology. 

The eastern Pinesnake (Pituophis melanoleucus) is found throughout eastern United States. Taxonomy in this group has been controversial with several conflicting species designations. Three subspecies of the eastern Pinesnake have prevailed in the literature to their geographic locations and scale coloration: the northern Pinesnake (P. m. melanoleucus), the Florida Pinesnake (P. m. mugitus), and the Black Pinesnake (P. m. lodingi). Within the region, there are several major barriers to dispersal, particularly major river drainage systems and human modification of the longleaf pine habitat. Consistently, a lack of phylogenetic resolution has plagued these taxa in prior studies. The goal of this study was to examine the taxonomic validity of the eastern Pinesnake complex using single nucleotide polymorphisms (SNPs) isolated from ultra-conserved elements (UCEs) in phylogenetic and population genetic approaches. Molecular species delimitation approaches indicated that the population of eastern Pinesnake exhibits population structure across its range that may rise to the level of being new species. 

Artificial intelligence (AI) for supporting second language (L2) writing processes and practices has garnered increasing interest in recent years, establishing AI-mediated L2 writing as a new norm for many multilingual classrooms. As such, the emergence of AI-mediated technologies has challenged L2 writing instructors and their philosophies regarding computer-assisted language learning (CALL) and teaching. Technologies that can combine principled pedagogical practices and the benefits of AI can help to change the landscape of L2 writing instruction while maintaining the integrity of knowledge production that is so important to CALL instructors. To align L2 instructional practices and CALL technologies, we discuss the development of an AI-mediated L2 writing technology that leverages genre-based instruction (GBI) and large language models to provide L2 writers and instructors with tools to enhance English for research publication purposes. Our work reports on the accuracy, precision, and recall of our network classification, which surpass previously reported research in the field of genre-based automated writing evaluation by offering a faster network training approach with higher accuracy of feedback provision and new beginnings for genre-based learning systems. Implications for tool development and GBI are discussed. 

Abstract The scale of data sets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions [BP] and Bayesian posterior probabilities [PPs]) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence data sets. As a consequence, today’s phylogenomic studies routinely report near-complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single data set contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that BPs and marginal PPs of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern phylogenomic data sets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic data sets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.[Big data; gene tree variation; genomic era; statistical bias.] 

Thinking With Algebra (TWA) is a National Science Foundation Project (DUE 2021414) to develop a post-secondary curriculum for intermediate algebra. TWA focuses on six elements that align with building algebraic fluency with conceptual understanding, a mixed review approach, small-group work, and whole-class discussion (Feikes, et al., 2021). Using an equity lens (Oppland-Cordell et al., 2024), TWA is designed for students, including underrepresented students, who need additional mathematical supports at the college level. Seventeen college math instructors attended a workshop on the lessons and pedagogy in order to use TWA in their college courses. Feedback from instructors participating in the TWA first-year faculty workshop indicated that the curriculum was used in many different ways to help prepare students for college algebra and other STEM courses. 

The Thinking With Algebra (TWA) curriculum, a National Science Foundation Project (DUE 2021414), applies a sociopolitical equity framework (Gutiérrez, 2012) to strengthen student participants’ mathematics achievement outcomes and mathematics identity development. Instructor and student data indicate improvements in these two dimensions. This project also expands knowledge about creating equitable math learning environments that support underrepresented students’ mathematical success. 

Algebra readiness and algebraic structure have been core ideas in our curriculum development work to help students develop conceptual understandings of algebra. This research brief uses Kaput’s (2008) definition of algebra to describe algebra readiness and algebraic structure, as they relate to thinking about algebra and thinking with algebra.