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ABSTRACT Fragment‐based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many‐body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose‐built to handle the combinatorial complexity of fragment‐based calculations. To fill this void we introduce “Fragme∩t”, an open‐source software application that provides a tool for community validation of fragment‐based methods, a platform for developing new approximations, and a framework for analyzing many‐body interactions.Fragme∩tincludes algorithms for automatic fragment generation and structure modification, and for distance‐ and energy‐based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q‐Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large‐scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non‐experts.Fragme∩tconforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsTheoretical and Physical Chemistry > ThermochemistrySoftware > Quantum Chemistry 

Summary Urbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited.Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering).Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions.Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales. 

Abstract The COVID‐19 pandemic significantly impacted undergraduate education and fundamentally altered the structure of course delivery in higher education. In field‐based biology and ecology courses, where instructors and students typically work collaboratively and in‐person to collect data, this has been particularly challenging. In this context, faculty from the Ecological Research as Education Network (EREN) collaborated with the National Ecological Observatory Network (NEON) to design five free‐flexible learning projects for use by instructors in varied modalities (e.g., socially distanced in‐person, remote, or HyFlex). The five flexible learning projects incorporated the Ecological Society of America’s 4DEE framework and included field data collection, data analysis components, and an activity that incorporates existing NEON field protocols or datasets. Each project was designed to provide faculty members with a high degree of flexibility so that they could tailor the implementation of the projects to fit course‐specific needs. Collectively, these learning projects were designed to be flexible, inclusive, and facilitate hands‐on research while working in alternative classroom settings.