Hello everybody,
I am modeling some plants with huge occurrence records (as many as 5,000) using maxent in R program. Due to sampling bias, spatial autocorrelation (SAC) in residuals of my maxent model are clearly evident (shown by the Moran’s I). It seems that the SAC is largely as a result of SAC in the variables that I used (bioclim and soil) and not SAC in the occurrence records. Despite using R packages that thin or reduce occurrence records in the best way possible (so as to reduce spatial autocorrelation) such as spThin, thin.max and spatialEco, I have been unable to the reduce spatial autocorrelation. I also used SAM to select spatial filters that were added to my model as a raster but this did not help. Could anyone suggest a function or a package that can help in reducing SAC in residuals of a maxent model. How could, I go about this.
Regards,
Jeff
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Jamie
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Yes, look into sample bias rasters. But I think you are misunderstanding SAC. If points are close in space, they will usually be correlated when considering a spatial predictor variable. I'm not sure what you meant when you said your points are not correlated but your variables are. The correlation is measured with the variables. You should see drops in SAC when you do spatial thinning or run things like SAM. Was this not the case?
Jamie
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