Predicting dispersal and recruitment of Miconia calvescens (Melastomataceae) in Australian tropical rainforests
Predicting dispersal and recruitment of Miconia calvescens (Melastomataceae) in Australian tropical rainforests
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DOI：
10.1007/s10530-008-9246-x
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发表时间：
2008-08-01
影响因子：
2.9
通讯作者：
Brooks, S. J.
中科院分区：
文献类型：
--
作者：
Murphy, Helen T.;Hardesty, B. D.;Brooks, S. J.
Miconia calvescens（Meconiacalvescens）是热带太平洋（包括夏威夷和大溪地群岛）的严重入侵者，目前对澳大利亚热带湿地的原生生物多样性构成了重大威胁。该物种是肉质果实，小种子和耐荫，因此有可能被广泛分散，并在相对完整的雨林栖息地招募，取代本地物种。了解和预测传播速度对于设计和实施有效的管理措施至关重要。我们使用了一个基于个人的模型，将来自类似的浆果本地物种的扩散曲线的扩散函数，和生育力和死亡率的生活史参数预测的空间结构的Miconia人口后30年的时间段。我们比较了模拟人口的空间结构，在北昆士兰州的热带雨林中的实际侵扰。我们的目标是评估如何以及模型预测实际的分散，并确定潜在的障碍和管道种子运动和幼苗建立。该模型过度预测了总体种群规模和实际侵扰的空间范围，预测个体发生在距离源最大1，750米处，而实际侵扰中任何检测到的个体的最大距离为1，191米。我们确定了几个管理入侵人群的特征，使模拟结果和实际的侵扰之间的比较困难。我们的研究结果表明，该模型的预测空间结构和传播的人口的能力将得到改善，通过纳入一个空间上明确的元素，分散和招聘概率，反映这些过程的景观的不同部分的相对适合性。
Miconia 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.