Predicting dispersal and recruitment of Miconia calvescens (Melastomataceae) in Australian tropical rainforestsExport / Share PlumX View Altmetrics View AltmetricsMurphy, H.T., Hardesty, B.D., Fletcher, C.S., Metcalfe, D.J., Wetcalfe, D.J. and Brooks, S. J. (2008) Predicting dispersal and recruitment of Miconia calvescens (Melastomataceae) in Australian tropical rainforests. Biological Invasions, 10 (6). pp. 925-936. Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link. Article Link: http://dx.doi.org/10.1007/s10530-008-9246-x AbstractMiconia calvescens (Melastomataceae) is a serious invader in the tropical Pacific, including the Hawaiian and Tahitian Islands, and currently poses a major threat to native biodiversity in the Wet Tropics of Australia. The species is fleshy-fruited, small-seeded and shade tolerant, and thus has the potential to be dispersed widely and recruit in relatively intact rainforest habitats, displacing native species. Understanding and predicting the rate of spread is critical for the design and implementation of effective management actions. We used an individual-based model incorporating a dispersal function derived from dispersal curves for similar berry-fruited native species, and life-history parameters of fecundity and mortality to predict the spatial structure of a Miconia population after a 30 year time period. We compared the modelled population spatial structure to that of an actual infestation in the rainforests of north Queensland. Our goal was to assess how well the model predicts actual dispersion and to identify potential barriers and conduits to seed movement and seedling establishment. The model overpredicts overall population size and the spatial extent of the actual infestation, predicting individuals to occur at a maximum 1,750 m from the source compared with the maximum distance of any detected individual in the actual infestation of 1,191 m. We identify several characteristic features of managed invasive populations that make comparisons between modelled outcomes and actual infestations difficult. Our results suggest that the model’s ability to predict both spatial structure and spread of the population will be improved by incorporating a spatially explicit element, with dispersal and recruitment probabilities that reflect the relative suitability of different parts of the landscape for these processes.
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