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1. Integration of Remote Sensing & Environmental Science in Placed-Based Learning

   Ignatius, A.R.; Flood, M.G (January 2021, Proceedings of the ASPRS 2021 Annual Conference)

   null (Ed.)

   Integration of remote sensing techniques and Environmental Science methodologies in place-based curriculum design creates unique learning opportunities. To promote introductory-level student engagement with STEM, our team designed a set of multidisciplinary teaching materials to intensely examine a single location: the Lake Sidney Lanier watershed of North Georgia, USA. Using a combination of scientific approaches from a variety of disciplines, course exercises encourage students to holistically learn about environmental conditions within the watershed. In addition, the learning materials require students to contemplate the process of knowledge-formation by considering the limitations and potential applications of different scientific approaches. Remote sensing exercises are embedded throughout the course content and include analysis of historic aerial imagery, Landsat-derived dynamic surface water extent, google timelapse land cover change, Sentinel 2 spectral bands, and evaluation of lidar-derived topography. Learning resources were intentionally designed to seamlessly integrate remote sensing approaches and traditional environmental science methods. Fundamental spatial concepts of scale and connectivity are considered using interdisciplinary approaches and local data. The environmental science theory of landscape ecology is presented alongside remote sensing concepts of spatial and temporal resolution. This allows students to think about the diverse ways scientists understand scale, pattern, and the definition of “place”. Multiple data sources are also provided for each topic. For example, remote sensing imagery is used to investigate surface water conditions during drought and high-rainfall time periods. In addition, USGS streamgage river discharge data and rainfall estimates are provided for students to examine drought history using multiple parameters. Lastly, sensor deployment and limitations of each data source are described so that students understand both the history of place as well as the process and development of science. Through the use of a place-based curriculum design and interdisciplinary lab exercises, students gain a holistic understanding of a regional watershed. 

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2. Completeness analysis for over 3000 United States bee species identifies persistent data gap

   https://doi.org/10.1111/ecog.06584  

   Chesshire, Paige R.; Fischer, Erica E.; Dowdy, Nicolas J.; Griswold, Terry L.; Hughes, Alice C.; Orr, Michael C.; Ascher, John S.; Guzman, Laura M.; Hung, Keng‐Lou James; Cobb, Neil S.; et al (May 2023, Ecography)

   Native bee species in the United States provide invaluable pollination services. Concerns about native bee declines are growing, and there are calls for a national monitoring program. Documenting species ranges at ecologically meaningful scales through coverage completeness analysis is a fundamental step to track bees from species to communities. It may take decades before all existing bee specimens are digitized, so projections are needed now to focus future research and management efforts. From 1.923 million records, we created range maps for nearly 88% (3158 species) of bee species in the contiguous United States, provided the first analysis of inventory completeness for digitized specimens of a major insect clade, and perhaps most important, estimated spatial completeness accounting for all known bee specimens in USA collections, including undigitized bee specimens. Completeness analyses were very low (3–37%) across four examined spatial resolutions when using the currently available bee specimen records. Adding a subset of observations from community science data sources did not significantly increase completeness, and adding a projected 4.7 million undigitized specimens increased completeness by only an additional 12–13%. Assessments of data, including projected specimen records, indicate persistent taxonomic and geographic deficiencies. In conjunction with expedited digitization, new inventories that integrate community science data with specimen‐based documentation will be required to close these gaps. A combined effort involving both strategic inventories and accelerated digitization campaigns is needed for a more complete understanding of USA bee distributions. 

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3. On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data

   Roth, Mark; Hallman, Tyler; Robinson, W. Douglas; Hutchinson, Rebecca A. (July 2021, ICML 2021 Workshop: Tackling Climate Change with Machine Learning)

   This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and temporal scales unachievable by expert surveys alone, but they lack the structure imposed in smaller scale studies to allow adjustments for observational biases. Spatial clustering approaches can construct the necessary structure after surveys have occurred, but more work is needed to ensure that they are effective for this purpose. In this proposal, we describe the role of spatial clustering for realizing the potential of large biodiversity datasets, how existing methods approach this problem, and ideas for future work. 

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4. The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

   https://doi.org/10.1098/rsta.2023.0057  

   Gerstner, Beth E; Blair, Mary E; Bills, Patrick; Cruz-Rodriguez, Cristian A; Zarnetske, Phoebe L (April 2024, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Dainton, John (Ed.)

   Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’. 

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5. Open Source Software for Spatial Data Science

   https://doi.org/10.1111/gean.12339  

   Anselin, Luc; Rey, Sergio J. (June 2022, Geographical Analysis)

   Much progress has been made in the development of software tools for spatial analysis since the special issue ofGeographical Analysisappeared in 2006, devoted to “Recent advances in software for spatial analysis in the social sciences” (Rey and Anselin 2006). The 15 some years since the publication of the issue have been marked by major changes in the spatial analytical software landscape. Arguably, three important and somewhat related phenomena can be distinguished that drove these changes: the embedding of spatial analysis into spatial data science; the growing recognition of open science/open source principles in empirical work; and the increasing adoption of a literate programming perspective. 

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