Hi all,
I have been using maxent jar software for generating background points for my true presences. I actually am a geostatistician and wanted to apply kriging prediction method to generate prediction maps for certain species. However, I realized that with maxent, the best number of background points is 10,000 as argued by many authors. Now 10000 backgound points plus actual presences make it a huge number, not feasible for distance based methods such as kriging. It's hard to get a sense of true absence probability due to multiple reasons. So I started experimenting on the number of background points. I generated multiple datasets starting from a small number of background points taking it to 10,000 gradually. For example for a data of 386 presences I used 15, 30, 50, 100, 200, 300, 386(equal), 400, 500, ....., 2000, ...., 3000, ....5000,..., 10000. I had to look at the geostatistical properties of the spatial structure (variogram) and maps of each dataset. I realized that only a moderate number of background points (equivalent to number of presences) gives the best spatial structure modeling. My colleague then helped me prepare a table of AUCs, Kappa and TSS, for all datasets, in which dataset with equal number of presences and background points gave the highest kappa and TSS values, supporting my geostatistical results of stronger spatial structure.
What I am asking here is "am I doing it correctly"? Are my findings make sense?
Also, is there any R code which can compute both Kappa and TSS from Maxent output?
Cheers,
Asad
PS: I have been fulfilling all the requirements for Maxent, such as bias file, environmental predictors etc.