Corn Pollen Dispersal: Quasi-Mechanistic Models and Field Experiments

Abstract

To make quantitative predictions about the pollen dispersal of a plant species under different environmental conditions, it is necessary to determine its individual pollen dispersal function, i.e., the two‐dimensional density function describing the probability that a pollen grain emitted in (0, 0) fertilizes an ovule in (x, y). This function will depend on biological and climate parameters. We present models for the individual dispersal function of corn. These models are based on Brownian motion with drift and integrate biological (difference of height between male and female flowers) and aerodynamic (settling velocity, wind speed, air turbulence) parameters. The models presented differ in the importance of vegetation in stopping the paths of pollen grains. The models were fitted to data from two large field experiments of corn using the color of kernels as a phenotypic marker for pollen dispersal. The resulting estimations for the parameters of the models and comparisons between models indicate that (1) these models can provide good predictions of the observed data, (2) vegetation is not the major obstacle that stops pollen paths, and (3) there is a benefit in considering the difference in height between male and female flowers. Furthermore, values of the parameters estimated from dispersal data appear consistent with meteorological and biological data acquired independently. Corresponding Editor: S. T. Jackson.

Patchy patterns due to group dispersal

“…(5). It has to be noted that this result is obtained without drift and gravity and without scaling parameters contrary to the results provided in Stockmarr (2002) and Klein et al (2003). Here, the marginal p.d.f.…”

“…The dispersal kernel generally decreases along the radial directions (Austerlitz et al, 2004;Klein et al, 2006a;Tufto et al, 1997) and can be anisotropic (Klein et al, 2003;Soubeyrand et al, 2007Soubeyrand et al, , 2008. If group dispersal occurs, then propagule transports are not independent anymore.…”

“…Remark: Klein et al (2003) and Stockmarr (2002) represent particle transports as 3D Brownian motions, the vertical dimension being used to define the times when the motions are stopped. Here, the particle motions 6 are stopped with the use of the final locations X j of the group centers.…”

Neutral modelling of agricultural landscapes by tessellation methods—Application for gene flow simulation

“…The sub-model for pollen dispersal was adapted from [25] and it accounts for distance between source and receptor plants as well as wind direction and intensity, and as plant height. To focus on the impact of spatial characteristics of landscapes, we kept agronomic and climatic inputs constant and identical for the GM and the conventional varieties.…”

A Lévy-flight diffusion model to predict transgenic pollen dispersal

“…At least 80% of flowering plants are directly dependent on insect pollinators [8], of which bees (honeybees in particular) are the most prolific. While pollination by wind is well understood and can be modelled using high-resolution atmospheric-dispersion models [9][10][11], modelling pollination by insects is more difficult [12]. This difficulty is partly due to the complexity of insect displacement patterns.…”