How far can a hawk's beard fly? Measuring and modelling the dispersal of <i>Crepis praemorsa</i>

Abstract

Summary We measured and modelled the dispersal of a wind‐dispersed herb, the leafless hawk's beard ( Crepis praemorsa , Asteraceae), using a combination of measurement techniques, selected empirical models and mechanistic models originally developed for trees. Dispersal was measured by releasing individual seeds and by placing seed traps around an experimentally created point source of seeding plants. Dispersal distances varied considerably between the experiments. In the seed releases, dispersal distances were positively related to horizontal wind speed (linear regression, P &lt; 0.001) and, under favourable conditions, many seeds dispersed over several metres, with a few at &gt; 30 m. Four empirical models (the inverse power (IP), the negative exponential (NE), Bullock &amp; Clarke's mixed model (MIX) and Clark et al .'s 2Dt model) were fitted to the data. IP and NE models often failed to accommodate the shape of the empirical distribution of dispersal distances, and the MIX model, although extremely flexible, tended to overfit the data. The 2Dt model was, however, flexible and realistic in each experiment. Nevertheless, the parameter values for all of the empirical models varied dramatically between experiments; no set of parameter values predicted the observed dispersal distances under all conditions. Two mechanistic models (Greene &amp; Johnson's analytical model (GJ) and Nathan et al .'s individual‐based simulation model (NSN)), originally developed for trees, were parameterized using independent data and parameter values from the literature. Although the NSN model provided a poor fit for the seed trap experiment, it performed almost as well as the best empirical models in the seed release experiments. Its predictions were further improved by including convection, and predicted dispersal &gt; 30 m corresponded closely with our observations. The prediction of low (&lt; 1%) dispersal &gt; 200 m requires further validation. We conclude that dispersal models for wind‐dispersed trees can be adapted for herbs with different dimensions and diaspore characteristics. Mechanistic simulations are superior because they are robust to environmental heterogeneity, as well as being informative in terms of understanding and predicting the effects of species characteristics and ecological factors on dispersal distances. Future empirical studies are needed at a wide range of environmental conditions, with careful measurement of conditions such as the strength and variability of horizontal and vertical wind speeds.