Inflated response curves and environmental overlap masks for species distribution models SDMs (R codes). Here you can download the R codes for inflated SDM response curves and environmental overlap masks introduced in Zurell et al. (2012) DDI. These simple functions facilitate visualisation of multi-dimensional SDM response and of model extrapolations to novel (=unsampled) environments. Inflated response curves are an extension of conventional partial dependence plots that show the effects of one variable on the response while accounting not only for the average effects of all other variables but also for minimum and maximum (and median and quartile) values. Thus, they are basically an abstracted 2D version of the multidimensional response surfaces. Their advantages are that (1) they are explicit about the shape of the response at different values of all other variables, and (2) make the responses clear if interactions are (implicitly or explicitly) included in SDMs. Environmental overlap masks compare two datasets (sampled data and prediction data, e.g. climate change scenario or different geographical area) and identify novel environments, meaning both environmental conditions beyond the sampled ranges of the single variables and novel combinations of environmental variables.
R codes are available for the functions (AppendixS1-functions.r) and for an example using simulated data (AppendixS1.r).
More info on how the R functions inflated.response() and eo.mask() work, can be found in the Online Supplementary material of the published article (DDI_887_sm_AppendixS2-S4.pdf).
Reference: Zurell D, Elith J, Schröder B (2012). Predicting to new environments: tools for visualising model behaviour and impacts on mapped distributions. Diversity and Distributions 18:628-634.