[46] James W Morley, Rebecca L. Selden, Robert J. Latour, Thomas L. Frölicher, Richard J. Seagraves, and Malin L. Pinsky. Projecting shifts in thermal habitat for 686 species on the north american continental shelf. PLoS ONE, 13, 2018.
[47] Tim Newbold. Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models. Progress in physical geography, 34(1):3-22, 2010.
[48] Census Bureau of USA. https://www.census.gov/geographiesmapping-files/time-series/geo/carto-boundary-file.html, n.d. Accessed: 2023-06-06.
[49] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017.
[50] Steven J Phillips et al. A brief tutorial on Maxent. AT&T Research, 190(4):231-259, 2005.
[51] Kathleen L. Prudic, Kent P. McFarland, Jeffrey C. Oliver, Rebecca A. Hutchinson, Elizabeth C Long, Jeremy T. Kerr, and Maxim Larrivée. ebutterfly: Leveraging massive online citizen science for butterfly conservation. Insects, 8, 2017.
[52] Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. A general purpose neural architecture for geospatial systems, 2022.
[53] Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uytendaele, and Trevor Darrell. Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning, 2023.
[54] David R Roberts, Volker Bahn, Simone Ciuti, Mark S Boyce, Jane Elith, Gurutzeta Guillera-Arroita, Severin Hauenstein, José J Lahoz-Monfort, Boris Schröder, Wilfried Thuiller, et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40(8):913-929, 2017.
[55] Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, and Solomon Hsiang. A generalizable and accessible approach to machine learning with global satellite imagery. Nature communications, 12(1):1-11, 2021.
[56] Judit K. Szabo, Lucas Rodriguez Forti, and Corey T. Callaghan. Large biodiversity datasets conform to benford's law: Implications for assessing sampling heterogeneity. Biological Conservation, 280:109982, 2023.
[57] Wilco CEP Verberk, Gerard Van Der Velde, and Hans Esselink. Explaining abundance-occupancy relationships in specialists and generalists: a case study on aquatic macroinvertebrates in standing waters. Journal of Animal Ecology, 79(3):589–601, 2010.
[58] Gabriel Vianna, Mark Meekan, Tova Bornovski, and Jessica Meeuwig. Acoustic telemetry validates a citizen science approach for monitoring sharks on coral reefs. *PloS one*, 9:e95565, 04 2014.
[59] Jacob Walker and Philip Taylor. Using ebird data to model population change of migratory bird species. Avian Conservation and Ecology, 12, 06 2017.
[60] Robert J. Warren et al. Universal ecological patterns in college basketball communities. PLoS One, 6(3):e17342, 2011.
[61] Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Aditya Singh, Sarah Graves, and Ethan White. A remote sensing derived data set of 100 million individual tree crowns for the national ecological observatory network. eLife, 10, 02 2021.
[62] Jing Wu, David Pichler, Daniel Marley, David Wilson, Naira Hovakimyan, and Jennifer Hobbs. Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis. arXiv preprint arXiv:2303.02460, 2023.
[63] Yifei Xue and Andrew Skidmore. Automatic counting of large mammals from very high resolution panchromatic satellite imagery. Remote Sensing, 9, 08 2017.