Hi guys, I would love to know how I could go about solving the above question. I currently have 2 different species distribution models, one created with the current climate projection and the other using a future climate projection. I already know how to use R to calculate the 3 different statistics which concerns niche overlap (Schoener's D/WIlliam's I/Rank correlation) using ENMTools package. Now I would love to find out how I could get an absolute value of the average change in habitat suitability in the SDM (concerning each pixel I guess). I know I could potentially use ArcMAP and utilise raster calculator and zonal statistics as a table functions to find this change but is there a shorter way I could go about this?
Thank you!
Hello Noh,
sure, you can do the same in R using raster::calc() or raster algebra (i.e. future - present, if future and present are RasterLAyers with the same extent/resolution) and raster::cellStats().
HTh,
Ákos
Dear Noh Ismail
I used to calculate the changes in the projected ecological range of the target species between the current and future climatic scenarios in class correspondence as follows: MaxEnt ASCII output projections will be converted using ArcGIS to raster layers with float data type, then the number of cells (pixels) between the projected climatic range will be calculated using zonal statistics in spatial analyst tools in ArcGIS
Maybe our article will help you to compare your two raster map outputs quantitively:
Article A novel approach to optimize hierarchical vegetation mapping...
You may appreciate that number of units ("ranges") impacts on the outcome.
Dear Noh,
As Joaquín suggested, have a look at the biomod_rangeSize function in the R package biomod2.
Best wishes
Pietro
Dear Joaquín Duque Lazo and Pietro Milanesi ,
I tried this function but was unable to get any results. Converted my tiff filed into .grd files and reuploaded it back into R, while including them into a raster stack but was unable to get anything. used the following code to process it =
test_curr
Dear Ákos Bede-Fazekas ,
I tried your method and was able to get the average change by using raster::cellStats(x, stat = 'mean'). In any case, if I was interested to calculate standard error via cell statistics on R, is there any way around it? I tried inputing a manual code for standard error and proceed with cell stats using this few line of codes :
> se_calc se_calc
function(x){sd(x,na.rm=T)/sqrt(length(x))}
> cell_suitchange_se cell_suitchange_se
Dear Noh,
Yes, you can calculate the standard error with cellStats(). The raster package is not well documented, this extra information is missing from the help page: the function defined by parameter 'stat' should accept the 'na.rm' (or the '...') parameter. So this solution should work:
se_calc
Dear Ákos Bede-Fazekas ,
Thank you so much for the prompt reply! The code you gave actually worked and I'm able to obtain the standard error! Really appreciate the help Ákos, Merry Christmas and a blessed new year ahead!
Best regards,
Noh
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