Dear list:
I am learning SDMs recently and got one questions and I am sure I can
get the right answer here.
For a certain area, the prediction of species distribution might
dependent on the input of the background points, ie., the points that
we would like to know if one or more species could reside. Two
strategies can be seen in literatures providing the input background
points: regular grids and random points. In the first case, all the
grids that contain the predictor information of the whole research
area are included in the analysis as data points, result in a regular
point matrix, such as in MAXENT.. While in the latter case, random
number of points were generated within the area and assigned the
values of the predictor they overlay, such as the algorithm employed
in BIOMOD. It seems that in BIOMOD, we can also implement such regular
grid as input background points, but the question for me is that I do
not know which strategy make sense in SDMs.
I think random sampling of background points are both included in
MAXENT and in algorithms that BIOMOD implemented. So, the main
difference between the two strategies might be:
1) Whether more than 1 point fall inside one grid. In regular grid,
all point fall inside different grid as guaranteed by the generation
procedure, but in random points, extra steps should be used to exclude
the closely dispersed points.
2) The stability of the model result. In regular grid, each grid
get it prediction from the model, given the same model, the output
distribution is always the same. While in random points, all the
random points get the prediction from the model, and the final
distribution map might different given the same model when the points
are arranged differently.
If these are correct, then regular grid as input points should be
superior than random. But I am not confident on this. Can somebody
help me out?
Best wishes~
Marco