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The geographical area occupied by a species is a valuable measure for assessing its conservation status. Coarse-grained occupancy maps are available for many taxa, e.g., as atlases, but often at spatial resolutions too coarse for conservation use. However, mapping occupancy at fine spatial resolution across the entire extent of the species' distribution is often prohibitively expensive for the majority of species. Occupancy downscaling is a technique to estimate finer scale occupancy from coarse scale maps, by using the occupancyarea relationship (OAR) which reflects how the proportion of area occupied increases with spatial grain size. Models that describe the OAR are fitted to observed occupancies at the available coarse-grain sizes and then extrapolated to predict occupancy at the finer grain sizes required. The downscale package in the R programming environment provides users with easy-to-use functions for downscaling occupancy with ten published models. First, upgrain calculates occupancy for multiple grain sizes larger than the input data. Normal methods for aggregating raster data increase the extent of the focal area as grain size increases which is undesirable, so the function fixes the extent for all grain sizes, assigning unsampled cells as absences. Four suggested methods are provided to enable this and upgrain.threshold provides diagnostic plots that allow the user to explore the inherent trade-off between making assumptions about unsampled locations and discarding information from sampled locations. downscale fits nine possible models to the data generated from upgrain. hui.downscale fits the special case of the Hui model. predict and plot extrapolate the fitted models to predict and plot occupancy at finer grain sizes. Finally, ensemble.downscale simultaneously fits two or more of the downscaling models and calculates mean predicted occupancy across all selected models. Here we describe the package and apply the functions to atlas data of a hypothetical UK species.