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Crispo, E.

Publications and source records attributed to Crispo, E..

2 recordsLinked to original sources

Genome-wide signatures of reproductive isolation shape the varied genomic landscape of the roundtail horned lizard (Phrynosoma modestum)

Population divergence is promoted and inhibited by gene flow and divergent selection, but the mechanisms of and relationship between these two processes remain poorly understood. Modelling the selective pressures at play in a natural population requires a thorough understanding of species structure and demographic history, which can be helpful in hypothesizing the genetic and evolutionary factors that underlie divergence. In this study, we assess whole genome sequences of round-tailed horned lizards (Phyrnosoma modestum) from throughout the species range and combine phylogenetic analyses with genetic landscape scans to understand how current genetic diversity has been influenced by demographic histories and evolutionary pressures. Maximum likelihood phylogenetic analysis supports two lineages within the species, correlating with a North/South population divide that likely diverged about 7 Ma and displayed little migration. Intermediate gdi values indicate that the two lineages may be in the gray area of speciation, yet our results also support significant isolation-by-distance (IBD). Negative values of Tajimas D offer support for selection acting on P. modestum, but may stem from recent population expansions. Genomic diversity within populations and islands of divergence between populations are also detected across the P. modestum genome, specifically exhibiting differentiation patterns linked to reproductive isolation and within-population selection. We posit potential explanations for the genomic landscape characterized here, namely that allopatry plays a large role in shaping genomic divergence across the species range. Taken together, our results provide a picture of a species currently maintaining species integrity despite significant genomic signatures of geographic structure and reproductive isolation.

evolutionary biology↗

Training Data: How can we best prepare instructors to teach data science in undergraduate biology and environmental science courses?

There is a clear and concrete need for greater quantitative literacy in the biological and environmental sciences. Data science training for students in higher education necessitates well-equipped and confident instructors across curricula. However, not all instructors are versed in data science skills or research-based teaching practices. Our study sought to survey the state of data science education across institutions of higher learning, identify instructor needs, and illuminate barriers to teaching data science in the classroom. We distributed a survey to instructors around the world, focused on the United States, and received 106 complete responses. Our results indicate that instructors across institutions use, teach, and view data management, analysis, and visualization as important for students to learn. Code, modeling, and reproducibility were less valued by instructors, although there were differences by institution type (doctoral, masters, or baccalaureate), and career stage (time since terminal degree). While there were a variety of barriers highlighted by respondents, instructor background, student background, and space in the curriculum were the greatest barriers of note. Interestingly, instructors were most interested in receiving training for how to teach code and data analysis in the undergraduate classroom. Our study provides an important window into how data science is taught in higher education as well as suggestions for how we can best move forward with empowering instructors across disciplines.

scientific communication and education↗