After more than a century of research the typical growth pattern of a tree was thought to be fairly well understood. Following germination height growth accelerates for some time, then increment peaks and the added height each year becomes less and less. The cross sectional area (basal area) of the tree follows a similar pattern, but the maximum basal area increment occurs at some time after the maximum height increment. An increase in basal area in a tall tree will add more volume to the stem than the same increase in a short tree, so the increment in stem volume (or mass) peaks very late. Stephenson et al. challenge this paradigm, and suggest that mass increment increases continuously. Their analysis methods however are a textbook example of the 'ecological fallacy', and their conclusions therefore unsupported.

Summary1 Spatial distributions of tropical trees often correlate with environmental variation, suggesting that ecological sorting caused by niche differentiation may be important for maintaining species diversity. 2 Four soil types have been identified in a 52-ha forest dynamics plot in Bornean mixed dipterocarp forest (ranked by increasing fertility and moisture: sandy loam, loam, fine loam, and clay). The distributions of 73% of tree species in the plot are significantly aggregated on one of these soil types. We tested the hypothesis that variation in performance (growth and mortality) underlies these edaphically biased species distributions. 3 Annual growth and mortality rates over 5 years were estimated for trees ≥ 1 cm in diameter and compared among soil types, life histories and species-aggregation patterns. 4 Overall, growth and mortality rates were lowest on the poorest soil (sandy loam). Growth rates on each soil type correlated with soil fertility for pioneers, while mortality rates correlated with soil fertility for both pioneers and late-successional species. 5 There was little evidence that soil specialists had a home-soil performance advantage. Soil-specific ranks of growth and mortality rates of each species-aggregation group largely mirrored the ranks of their rates across the plot and did not shift substantially among soil types. On every soil, species aggregated on sandy loam or clay ranked last or next-to-last, and species aggregated on loam ranked the highest. 6 Ecological sorting of species among soils was strong. With increasing diameter, species were lost from the soils on which they were not aggregated more frequently than would be expected based on random mortality. The underlying mechanisms of ecological sorting may involve low mortality rates as a requirement for species to achieve high abundance on the poorest soil, whereas for the richer soils, having high growth rates appears relatively more important for achieving high abundance. 7 Thus, species' demographic responses to resource variation among soil types, especially related to the poorest soil, affects tree species distribution patterns in this forest and thereby influences the structure of tropical forest communities.

An overview of the classes of mechanisms causing recruitment limitation, which includes source limitation, dissemination limitation and establishment limitation, is presented. The three mechanisms of dissemination limitation are discussed in detail with emphasis on how the behaviours and physiologies of animal seed dispersers may lead to dissemination limitation. The different consequences of dissemination limitation for patterns of recruitment in plant populations and how these patterns in turn affect plant community structure are also discussed, including the ways on how dissemination limitation can contribute to the origin and maintenance of species diversity in ecological communities.

Seed dispersal fundamentally influences plant population and community dynamics but is difficult to quantify directly. Consequently, models are frequently used to describe the seed shadow (the seed deposition pattern of a plant population). For vertebrate-dispersed plants, animal behavior is known to influence seed shadows but is poorly integrated in seed dispersal models. Here, we illustrate a modeling approach that incorporates animal behavior and develop a stochastic, spatially explicit simulation model that predicts the seed shadow for a primate-dispersed tree species (Virola calophylla, Myristicaceae) at the forest stand scale. The model was parameterized from field-collected data on fruit production and seed dispersal, behaviors and movement patterns of the key disperser, the spider monkey (Ateles paniscus), densities of dispersed and non-dispersed seeds, and direct estimates of seed dispersal distances. Our model demonstrated that the spatial scale of dispersal for this V. calophylla population was large, as spider monkeys routinely dispersed seeds)100 m, a commonly used threshold for long-distance dispersal. The simulated seed shadow was heterogeneous, with high spatial variance in seed density resulting largely from behaviors and movement patterns of spider monkeys that aggregated seeds (dispersal at their sleeping sites) and that scattered seeds (dispersal during diurnal foraging and resting). The single-distribution dispersal kernels frequently used to model dispersal substantially underestimated this variance and poorly fit the simulated seed-dispersal curve, primarily because of its multimodality, and a mixture distribution always fit the simulated dispersal curve better. Both seed shadow heterogeneity and dispersal curve multimodality arose directly from these different dispersal processes generated by spider monkeys. Compared to models that did not account for disperser behavior, our modeling approach improved prediction of the seed shadow of this V. calophylla population. An important function of seed dispersal models is to use the seed shadows they predict to estimate components of plant demography, particularly seedling population dynamics and distributions. Our model demonstrated that improved seed shadow prediction for animal-dispersed plants can be accomplished by incorporating spatially explicit information on disperser behavior and movements, using scales large enough to capture routine long-distance dispersal, and using dispersal kernels, such as mixture distributions, that account for spatially aggregated dispersal.