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
algorithms that BIOMOD implemented. So, the main difference between the two
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.

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Chinese Ecological Research Network,
Institute of Geographical Sciences and Natural Resources Research,
 Chinese Academy of Sciences,
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