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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 Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL.