spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

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Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “…duplicate occurrences within each predictor raster grid cell, occurrences were further thinned using the thin function in the R package “spThin” (Aiello-Lammens et al., 2015), which utilizes a randomization algorithm to return the highest number of occurrences for a specified thinning…”
Comparing sample bias correction methods for species distribution modeling using virtual species (2021, background): “Moreover, it is geographic sampling bias that is readily apparent to practitioners and has been the focus of many studies (Lobo and Tognelli 2014, Aiello-Lammens et al. 2015, Fernández and Nakamura 2015, Soley-Guardia et al. 2016, Qiao et al. 2017).”
Climate change vulnerability assessment of species (2018, neutral): “Other approaches include Bayesian approaches (Manceur & Kühn, 2014; Rocchini et al., 2017), subsampling in geographic space (Aiello-Lammens et al., 2015) or in environmental space (Varela, Anderson, & Fernández-González, 2014), and weighting presences by the inverse of their density (Stolar &…”