EXPLORATORY PATH-ANALYSIS WITH APPLICATIONS IN ECOLOGY AND EVOLUTION

In this article, I first describe some recent developments in the iden tification of the structure of dependencies among variables in multiva riate data relevant to exploratory path analysis. I then introduce a b ootstrap modification of one important method (the SGS algorithm) that is designed to improve error rates of exploratory path analysis in th e small data sets that are typical of studies in ecology and evolution . Monte Carlo results indicate that this modified technique can find p ath models that are close to the true model even in very small data se ts. The bootstrapped SGS algorithm is then applied to a previously pub lished data set involving attributes affecting seed dispersal in St. L ucie's cherry.