I build a species distribution model with data from location A and tested it on location B. The picture shows a rastermap of location B and what I guess should be the model residuals.
- Green - are the areas where the model was right (value 0)
- Blue - are the areas where the model predicted species occurrence but there was no observation (value -1)
- Black - are the areas where the species was observed but the model predicted no occurrence (value 1)
Should I transform the Raster to points (one point each cel) and use this as basis for the calculation?
What should I do with the areas where the model was right (value 0)?
should I delete them before I perform the test?
This question is related to Calculating residuals of species distribution model and test for spatial auto correlation at Cross Validated.
I asked the question because I always read that you have to take care of spatial autocorrelation in the analysis of species distributional data when building a SDM:
"Species distributional or trait data [...] often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. "(Dormann, 2007).
However I cannot figure out whether it is an issue in my case. I did an systematic, full sampling of species occurrence/absence and environmental variables in Area A. This data (not georeferenced) I used to build a deterministic model to predict species distribution based on habitat preference. I just built habitat preference curves for each predictor based on species occurrence and habitat availability at each sample point and combined the habitat preferences for each predictor to a combined preference by multiplication. @Jeffrey Evans: this could also be interpreted as occurrence probability.
Then I applied the model to the independent area B, where I have a grid/raster of the environmental variables. I also mapped the species occurrence in B to test the model against independent data. I tested the occurrence probability against the observed occurrences. My AUC values are good (around 0.8).
Now I wanted to see whether there is some kind of spatial pattern in the residuals of the model. Iam not even sure whether I should call it residuals because i didn't build a statistical model and do the test against independent data. Maybe better call it error, the error between observed and predicted occurrence. My understanding is that when there is an error which shows spatial autocorrelation then my model omits an important predictor or process that explains the spatial distribution
My main worry is whether I can I trust the AUC value and assess the predictive performance as good?
Because I could not figure out on which data I should do the morans I test I askesd the initial question.