Publikationsserver der Universitätsbibliothek Marburg

Titel:Modelling dispersal in plants
Autor:Niggemann, Marc
Weitere Beteiligte: Ziegenhagen, Birgit (Prof. Dr.)
URN: urn:nbn:de:hebis:04-z2010-00868
DDC:570 Biowissenschaften, Biologie
Titel (trans.):Modellierung der Ausbreitung bei Pflanzen


Modell, Ausbreitung, Pflanzen, simulation, Simulation, Dispersal, plants, Verbreitung, model

Dispersal is an essential process in plant survival because it allows plants as static organism to move. Plant dispersal is necessary to avoid unsuitable conditions and to colonise new sites. Additionally gene flow between populations reduces possible genetic threads as inbreeding depression. Genetic exchange is not limited to the dispersal of seeds, but is also possible by pollen dispersal. The most common dispersal vectors for plants are animals and wind. However, in human dominated landscapes animals could be replaced by humans. To assess the importance of humans on plant distribution a simulation model was created. The effect of human movement and gardening styles were tested for different ruderal plant species. I found strong influence of human movement behaviour especially on alien species. During 20 years these species extended their distribution range taking benefit from increased human mobility as revealed by sociological studies. In contrast native species tended to be more affected by the change of the agricultural landscape and of rural to urban life styles. Nevertheless, the change in species distribution could in all cases be better explained by simulating human transport based on human movement behaviour than by a distance dependent diffusion model. Another approach using simulation models for dispersal has been done in the wind pollinated floodplain species Populus nigra in Central Germany. The aim of the study was to assess the extent of intraspecies respectively introgressive pollen mediated gene flow by the hybrid P. x canadensis. Therefore the estimation of a pollen dispersal kernel was essential to model the dispersal processes. Genetic paternity analyses allow the calculation of pollen dispersal distances between father and mother trees. I used marked point pattern analyses to estimate and assess the uncertainty in fitted pollen dispersal kernels. There was only a significant departure from the null random mating model up to a distance of approximately 300m, although we were dealing with a wind pollinated species and the analyses were based on a huge dataset of more than 1,500 seeds. A two-component pollen dispersal kernel comprising an exponential power function and a truncated uniform function was the most parsimonious model to fit the data. The fitted kernel was able to predict the number of seeds fathered by a male tree and provided comparable results with published spatial statistic models. Our results strongly suggest that kernel estimates based on the direct fit of the observed mating distances differed considerably from fitted kernels that account for the spatial structure of adult trees. The uncertainty in the estimation of those directly fitted kernel may be underestimated. With a suitable pollen dispersal kernel at hand, simulation studies were conducted to understand the discrepancy between low introgression rates in the natural population of P. nigra and the overwhelming number of hybrids in the close vicinity. Using different standard dispersal kernels and our fitted two-component exponential power function, we found that the probability of fertilisation was 10-1 to 10-2 times lower in hybrid than in P. nigra pollen. This range could be confirmed with published results from hand pollination experiments and is the first time that the reproductive barrier between P. nigra and P. x canadensis has been revealed in open pollinated trees in a natural population. Using simulation models to understand different kinds of dispersal mechanisms turned out to be a promising way not only to describe the process of dispersal itself but also to combine expert knowledge of various disciplines in an ecological simulation model.

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