Scaling issues of neutral theory reveal violations of ecological equivalence for dominant Amazonian tree species - Edwin Pos; Juan Ernesto Guevara; Jean‐François Molino; Daniel Sabatier; Olaf S. Bánki; Nigel C.A. Pitman; Hugo F. Mogollón; Roosevelt García‐Villacorta; David Neill; Oliver L. Phillips; Carlos Cerón; Marcos Ríos Paredes; et al. (Ecology Letters, 2019)
Neutral models are often used as null models, testing the relative importance of niche versus neutral processes in shaping diversity. Most versions, however, focus only on regional scale predictions and neglect local level contributions. Recently, a new formulation of spatial neutral theory was published showing an incompatibility between regional and local scale fits where especially the number of rare species was dramatically under‐predicted. Using a forward in time semi‐spatially explicit neutral model and a unique large‐scale Amazonian tree inventory data set, we show that neutral theory not only underestimates the number of rare species but also fails in predicting the excessive dominance of species on both regional and local levels. We show that although there are clear relationships between species composition, spatial and environmental distances, there is also a clear differentiation between species able to attain dominance with and without restriction to specific habitats. We conclude therefore that the apparent dominance of these species is real, and that their excessive abundance can be attributed to fitness differences in different ways, a clear violation of the ecological equivalence assumption of neutral theory.

Estimating species richness in hyper‐diverse large tree communities - Hans ter Steege; Daniel Sabatier; Sylvia Mota de Oliveira; William E. Magnusson; Jean‐François Molino; Vitor F. Gomes; Edwin T. Pos; Rafael P. Salomão (Ecology, 2017)
Species richness estimation is one of the most widely used analyses carried out by ecologists, and nonparametric estimators are probably the most used techniques to carry out such estimations. We tested the assumptions and results of nonparametric estimators and those of a logseries approach to species richness estimation for simulated tropical forests and five data sets from the field. We conclude that nonparametric estimators are not suitable to estimate species richness in tropical forests, where sampling intensity is usually low and richness is high, because the assumptions of the methods do not meet the sampling strategy used in most studies. The logseries, while also requiring substantial sampling, is much more effective in estimating species richness than commonly used nonparametric estimators, and its assumptions better match the way field data is being collected.