Data from: No silver bullets in correlative ecological niche modeling: insights from testing among many potential algorithms for niche estimation

The field of ecological niche modeling or species distribution modeling has seen enormous activity and attention in recent years, in light of exciting biological inferences that can be drawn from correlational models of species' environmental requirements (i.e., ecological niches) and inferences of potential geographic distributions. Among the many methods used in the field, one or two are in practice assumed to be 'best' and are used commonly, often without explicit testing. We explore herein implications of the "No Free Lunch" theorem, which suggests that no single optimization approach will prove to be best under all circumstances: we developed diverse virtual species with known niche and dispersal properties to test a suite of niche modeling algorithms designed to estimate potential areas of distribution. The result was that (1) indeed, no single 'best' algorithm was found, and (2) different algorithms perform in very different manners depending on the particularities of the virtual species. The conclusion is that niche or distribution modeling studies should begin by testing a suite of algorithms for predictive ability under the particular circumstances of the study, and choose an algorithm for a particular challenge based on the results of those tests. Studies that do not take this step may use algorithms that are not optimal for that particular challenge.Details of characteristics of the 29 virtual species* Method to generate the virtual species - A virtual species is a group of occurrences, which are selected by an ellipsoid in a 3D environmental space. In this case, we did a principal component analysis (PCA) on the 19 bioclimatic variables, and used the first 3 principal components to create a 3D environmental space. The x, y, z axes are the first three principal components. All of these processes were implemented via Niche Analyst (http://biodiversity-informatics-training.org/software-data-sources/nichea/). Using Niche Analyst, we created two groups of virtual species with different volumes. The volume of 'big' group was 143.69, and the 'small' one was 2.14. Each group had 7 virtual species with the same centroid and same directions of semi-axes. * The distribution maps of virtual species - Once niches were created, we explored their projections into geographic space, and created different scenarios of abiotic factors in relation to dispersal. Specifically, we created a scenario in which all suitable conditions were accessible ("Hutchinson's Dream," or HD), one in which distributional limitation was driven by dispersal/access exclusively and not by abiotic conditions ("Wallace's Dream," or WD), and one in which both abiotic and dispersal considerations act to limit distributions in different areas ("Classic BAM," or CB).data.zi