Using genetic markers to estimate the pollen dispersal curve

Abstract

Pollen dispersal is a critical process that shapes genetic diversity in natural populations of plants. Estimating the pollen dispersal curve can provide insight into the evolutionary dynamics of populations and is essential background for making predictions about changes induced by perturbations. Specifically, we would like to know whether the dispersal curve is exponential, thin‐tailed (decreasing faster than exponential), or fat‐tailed (decreasing slower than the exponential). In the latter case, rare events of long‐distance dispersal will be much more likely. Here we generalize the previously developed twogener method, assuming that the pollen dispersal curve belongs to particular one‐ or two‐parameter families of dispersal curves and estimating simultaneously the parameters of the dispersal curve and the effective density of reproducing individuals in the population. We tested this method on simulated data, using an exponential power distribution, under thin‐tailed, exponential and fat‐tailed conditions. We find that even if our estimates show some bias and large mean squared error (MSE), we are able to estimate correctly the general trend of the curve — thin‐tailed or fat‐tailed — and the effective density. Moreover, the mean distance of dispersal can be correctly estimated with low bias and MSE, even if another family of dispersal curve is used for the estimation. Finally, we consider three case studies based on forest tree species. We find that dispersal is fat‐tailed in all cases, and that the effective density estimated by our model is below the measured density in two of the cases. This latter result may reflect the difficulty of estimating two parameters, or it may be a biological consequence of variance in reproductive success of males in the population. Both the simulated and empirical findings demonstrate the strong potential of twogener for evaluating the shape of the dispersal curve and the effective density of the population ( d e ).

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