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Overwintering monarch (Danaus plexippus) populations have declined since the 1990s. In response, restoration of milkweeds, including Asclepias syriaca (common milkweed), an important host plant in their breeding grounds, has become increasingly common. However, latitudinal variation in milkweed populations suggests the possibility of regional adaptation and the potential for seed provenance to affect restoration success. Using seeds from 20 populations throughout the range of A. syriaca, we tested whether seed mass, germination success, and germination time in the greenhouse demonstrate geographic clines consistent with available evidence for this species from other studies. In addition, we tested for patterns in germination traits consistent with adaptation to spring thermal conditions by planting seeds from 10 populations in growth chambers simulating Minnesota and Kentucky spring temperatures. Even after accounting for seed mass, seeds from higher latitudes germinated faster on average under all conditions. Elevated temperatures accelerated germination time and leaf development time; however, we did not detect geographic patterns in leaf development time, indicating that the processes underlying the latitudinal cline in germination time may be unique to the germination stage. In the thermal adaptation study, high-latitude populations produced larger seeds and seeds that germinated at a higher rate; however, neither latitudinal trend was observed in the geographic clines study, even though individual seed mass predicted germination success. High-latitude populations express more favorable germination traits in every setting measured, perhaps due to reduced dormancy. Consequently, we conclude that latitudinal clines are more consistent with adaptation to growing season length than to spring temperatures. 

Abstract There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.