I am running boosted regression trees (BRT) in R, with the package dismo and I have included a predictor (residual autocovariate) that, in theory, correct for spatial autocorrelation, following a paper from Crase et al (2012). My data units are grid-cells in vector format. I have defined binary neighbours (i.e. they all have the same weight. I don't have any reason to consider any other type) and of type 'queen' (i.e. those 8 neighbours that have any contact with each grid cell, in my case).
I'm using these BRT to relate environmental predictors to different biodiversity metrics (responses) at the global scale.
The thing is that, even after correcting in the way I exposed above, the residuals still have spatial correlation (measured as global Moran's I). I've used this approach before and never got this problem. So, I have two questions:
Is there any way to solve this issue
Is it that bad to have remaining spatial autocorrelation? I know global richness of species (for example) has this characteristic and, of course, all models are gonna miss some predictor in order to fully explain this natural clusterization of fauna
Any thought is welcome!