I like to understand processes causing the distribution and abundance of species. Tropical forest trees offer good tests, because there are a lot of species and they can be counted thoroughly; I have spent more than a decade building a network of scientists completing plots, and I have assembled data systems to handle the results. To understand observed patterns, the stochastic, individual-based approach of the neutral theory offers a basis for relating processes we can measure, like death and dispersal, to emergent community phenomena, such as abundances and distributions. I present data and modeling results on three topics 1) the species abundance distribution, 2) spatial patterns relative to dispersal, and 3) niche partitioning.  
Results/Conclusions

The neutral theory accurately predicts community-wide abundances, accounting for the large number of rare species, however, it is not neutrality that leads to the prediction, rather invasion-extinction dynamics from a diverse pool of species. I demonstrate that highly non-neutral models lead to the log-series abundance distribution, and there is theoretical work to support this. Theory has not gotten far, however, on spatial patterns. I show how dispersal and species abundance together predict range size, and conclude that tropical tree species are nearly all well-dispersed at the scale of 1 km. The same theory should apply at much larger scales, and I am assembling geographic range data on tropical trees (with a large team) to try to test it. I have also incorporated niche theory into the stochastic community model, and show how colonization-extinction dynamics interact with niche differences among species. I propose that high diversity of individual tropical forests is largely maintained by species input from a much larger (and very diverse) community, not by local niche differences. Large input of 'tourist' species might be hindering attempts to understand causes of abundance: local patterns may be largely driven by niche differences across continents and long time scales.