First you should use only variables with biological importance in the animal (case study) live. Then, you can use ENMTools to test collinearity between variables. Is easy, just take a look inside of ENMTools user manual (http://www.danwarren.net/enmtools/ENMTools_User_Manual%201.0.pdf)

Thank you Leopoldo!

I understand the method of ENMTool. But, how can I extract environment variables with biological importance? Should I extract environment variables of specific locality points? If so, how to extract them?

I have resolved the problem. Use DIVA-GIS. Thanks!

The raster package of R language has an extract function to obtain cell values from raster layers. You can "sanwich" the layers (stack them) and retrieve the points in a single step.

Hi Yue Li 

If you have resolved your question, can you please share the steps which you carried out (through Diva-GIS. It would be helpful. Thank you

I have the same problem. I'm working with arcgis and i can't extract that data. If someone knows the steps, please let me know.

thanks

Thanks Leopoldo! just used EMTools, really straight forward way to compare the ascii files! :)

H

I think that with DIVA-GIS is pretty easy. Once you have downloaded the 19 bioclimatic variables, you need to associate rasters to DIVA by selecting Tools > Options,  search the fold with .CLM files and then Apply. Then, go to Modeling>Evaluation>Sample points  with the occurrence shapefile activated. This will generate random points. To extract climate data from these points go to Data>extract value by points>from climate data. This will generate a txt that you can use on any statistical software to run correlations among variables.

Cheers!

use ENM tools

I want to check multicollinearity using pairwise correlation coefficient (r) in SPSS. Can somebody tell me how to format bioclim climate variables, (raster asc files) for this.

I use openModeller to extract cell values from raster layers for relative points and then implement the data in spss to estimate the correlation coefficient between layers.

Run a factor analysis (or PCA) before a bivariate correlation test to screen your variables and use only the finest.

Cheers

Variance Inflation Factors that calculates variance-inflation and generalized variance-inflation factors for linear, generalized linear, and many other statistical models.

In the below video link, the process has been explained well

https://www.youtube.com/watch?v=QruEcbgfhzo

Not only correlation/multicollinearity and the ecological interpretability (as mentioned by Leopoldo Ferreira de Oliveira Bernardi ) are important when you select from the available bioclimatic variables, but the way how the month/quarter the bioclimatic variable is based on will shift in time (and in space). Please refer to this paper:

Article The way bioclimatic variables are calculated has impact on p...

Pearson Correlation, Variance Inflation Factor (VIF), PCA.

An R package recently came out and compiling several methods for pre-processing, processing, and post-processing the data to model ecological niche

Worth checking out:

Article ENMTML: An R package for a straightforward construction of c...