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Hi, I’m interested in doing some niche modelling for a species that I’m studying. I’ve casually followed the use of ecological niche modelling and GARP over the past few years and am now starting to read up about it more. The more I read, however, the more I realize there are so many other newer alternative predictive tools available, such as the new WhyWhere algorithm or some of these novel techniques evaluated in the Elith et al. 2006 paper (e.g., MAXENT, BRT, mars-comm, etc.). It’s difficult to know which one to use? How would you go about deciding which way to go? If I felt that they all were very useful, I might feel more comfortable just sticking with DesktopGARP, but that was one of the lowest performing methods according to the Elith et al. paper. Any advice? Thanks!
Comment by Rob — July 14, 2006 @ 10:00 pm
Good question Rob but the answer has a lot of facets that I will try to do justice to next week in a longer reply. Think about the assumption in your question “It’s difficult to know which ONE to use?”. There are a number of dimensions across the models you might want to explore - classical vs heuristics, new vs. well researched, plus they more rightly exist in a benefit/cost framework, i.e. an optimal method in your situation might be something really simple that is average.
I think the OpenModeller group found a bug in the rule archiving part of DesktopGARP that increased the accuracy of the GARP algorithm to the medium group along with GLM and some others. You might want to look into that version but they would have the full story on that though. Then there is the fact that you get higher accuracy with a broader range of variables, particularly monthly temperature and rainfall instead of annual averages reported in S06. Time could be more efficiently spent getting predictor variables optimized.
I also don’t think the validation process used in Elith et.al. tells the full story either. An in-range random sample from highly autocorrelated data does little more that test goodness of fit. The real test of a niche model is on out-of-range problems, such as detection of new species or invasive species problems, of which there are many documented successes for DesktopGARP. Then there are the types of model not examined, based around k-means, such as reviewed here. I am not saying it is a flawed study, but that it is not complete.
I am the first to acknowledge that results can sometimes be dissapointing. Its not an exact science yet. You have to do a lot of testing.
Comment by davids — July 14, 2006 @ 10:18 pm