Incorporating Animal Behavior Into Seed Dispersal Models: Implications for Seed Shadows

Abstract

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.

Neighborhood effects on seed dispersal by frugivores: testing theory with a mistletoe–marsupial system in Patagonia

“…B.2). These patterns of seed deposition are consistent with what has been shown in elsewhere when dispersal is mediated by animals (e.g., Aukema 2004, Russo et al 2006, Hampe et al 2008.…”

Primate seed dispersal leaves spatial genetic imprint throughout subsequent life stages of the Neotropical tree Parkia panurensis

“…For zoochorously dispersed seeds, the behaviour and particularly the movement patterns of frugivores influence the shape and dimensions of the seed shadow (Jordano et al 2007;Karubian et al 2012;Russo et al 2006). This template ''defines what is possible for the seedling recruitment in a given area'' (Howe and Miriti 2004, p. 654), but the question arises whether and how this initial template also contributes to the recruitment of new reproductive adults into the population (Schupp and Jordano 2011).…”

Sleeping-tree fidelity of the spider monkey shapes community-level seed-rain patterns in continuous and fragmented rain forests

“…Previous studies on the spider monkey (Chapman et al 1989, Russo & Augspurger 2004, Russo et al 2006 The relatively higher variation in STF in forest fragments than in continuous forest sites can be related to food scarcity in forest fragments (Arroyo-Rodríguez & Mandujano and smaller home-range sizes in fragments (Chaves et al 2012). Thus, the spatial variation in resource availability is expected to be higher in forest fragments than in continuous forest.…”