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An R package that 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.
To install the package directly from github (rather than CRAN) run:
remotes::install_github("charliem2003/downscale", build_vignettes = TRUE)
The package provides three sets of functions for each stage of analysis:
upgrain and upgrain.threshold prepare
atlas data for downscaling.
downscale and hui.downscale model the
OAR to the prepared data for one of ten possible downscaling
models.
predict.downscale and
plot.predict.downscale take the model outputs and predict
occupancy at finer grains.
Finally, ensemble.downscale will run
downscale and 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")
Or are available through the github wiki:
Introduction to downscaling species occupancy
Upgraining atlas data for downscaling
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.
Author: Charles Marsh with input from Louise Barwell and Cang Hui. Maintainer: Charles Marsh charlie.marsh@mailbox.com Website: https://github.com/charliem2003/downscale
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.
Groom, Q., Marsh, C.J., Gavish, Y. Kunin, W.E. (2018). How to predict fine resolution occupancy from coarse occupancy data. Methods in Ecology and Evolution 9(11), 2273-2284.
Marsh, C.J, Barwell, L.J., Gavish, Y., Kunin, W.E. (2018). downscale: An R package for downscaling species occupancy from coarse-grain data to predict occupancy at fine-grain sizes. Journal of Statistical Software 86(Code Snippet 3), 1-20.
Marsh, C.J, Gavish, Y., Kunin, W.E., Brummitt N.A. (2019). Mind the gap: Can downscaling Area of Occupancy overcome sampling gaps when assessing IUCN Red List status? Diversity and Distributions 25, 1832-1845.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.