The package provides a set of functions that model the occupancy-area relationship (OAR) of known coarse scale data. The models are then extrapolated to predict the proportion of occupied area at finer grain sizes.
The package provides three sets of functions for each stage of analysis:
upgrain.threshold prepare atlas data for downscaling.
hui.downscale model the OAR to the prepared data for one of ten possible downscaling models.
plot.predict.downscale take the model outputs and predict occupancy at finer grains.
ensemble.downscale will run
predict.downscale for a number of selected downscaling functions and calculate the mean predicted occupancies across all models.
The general flow of the package, and the inputs required for each function, is as follows:
Two vignettes are available to guide users. Both work through examples in code:
vignette("Downscaling", package = "downscale")
vignette("Upgraining", package = "downscale")
This package was created as part of deliverable D3.2 of WP3 of the project: EU-BON: Building the European Biodiversity Observation Network - a 7th Framework Programme funded by the European Union under Contract No. 308454.
Charles Marsh with input from Louise Barwell and Cang Hui.
Maintainer: Charles Marsh <email@example.com>
For reporting bugs or requesting information please include 'downscale' in the subject line.
Azaele, S., Cornell, S.J., & Kunin, W.E. (2012). Downscaling species occupancy from coarse spatial scales. Ecological Applications 22, 1004-1014.
Barwell, L.J., Azaele, S., Kunin, W.E., & Isaac, N.J.B. (2014). Can coarse-grain patterns in insect atlas data predict local occupancy? Diversity and Distributions 20, 895-907.
Hui, C. (2009). On the scaling patterns of species spatial distribution and association. Journal of Theoretical Biology 261, 481-487.
Hui, C., McGeoch, M.A., & Warren, M. (2006). A spatially explicit approach to estimating species occupancy and spatial correlation. Journal of Animal Ecology 7, 140-147.