INCORPORATING ANIMAL BEHAVIOR INTO SEED DISPERSAL MODELS: IMPLICATIONS FOR SEED SHADOWS

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.