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Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users. 

Colorado is home to an incredibly rich community of native pollinating insects that contribute to the state’s economy and enhance Coloradans’ quality of life through the irreplaceable role they play in ecosystems. The pollination services these essential insects provide are at the heart of a healthy environment, contributing to our agricultural production and food systems, and relied upon by flowering plants across the state. In turn, flowering plants support the state’s wildlife, add color to the beautiful landscapes that we all treasure, and provide the basis for healthy functioning ecosystems. Despite their central importance, however, to date, no comprehensive assessment of the health of the state’s native pollinating insects has been conducted. Recognizing this need for coordinated state-level efforts to better understand the status and health of our native pollinating insects, Senate Bill 22-199, the Native Pollinating Insects Protection Study, was passed by the State Legislature and signed into law by Governor Jared Polis in May 2022. The Colorado Department of Natural Resources subsequently commissioned this study, awarded to a collaborative team of pollinator researchers, managers, and conservationists. The study was coordinated by Colorado State University Extension, in collaboration with the Xerces Society for Invertebrate Conservation and the University of Colorado Museum of Natural History, and in cooperation with leading experts in native pollinating insect ecology, management, and conservation. 

One of the defining features of the Anthropocene is eroding ecosystem services, decreases in biodiversity, and overall reductions in the abundance of once-common organisms, including many insects that play innumerable roles in natural communities and agricultural systems that support human society. It is now clear that the preservation of insects cannot rely solely on the legal protection of natural areas far removed from the densest areas of human habitation. Instead, a critical challenge moving forward is to intelligently manage areas that include intensively farmed landscapes, such as the Central Valley of California. Here we attempt to meet this challenge with a tool for modeling landscape connectivity for insects (with pollinators in particular in mind) that builds on available information including lethality of pesticides and expert opinion on insect movement. Despite the massive fragmentation of the Central Valley, we find that connectivity is possible, especially utilizing the restoration or improvement of agricultural margins, which (in their summed area) exceed natural areas. Our modeling approach is flexible and can be used to address a wide range of questions regarding both changes in land cover as well as changes in pesticide application rates. Finally, we highlight key steps that could be taken moving forward and the great many knowledge gaps that could be addressed in the field to improve future iterations of our modeling approach.