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Landscape variables impact the structure and composition of butterfly assemblages along an urbanization gradient

机译:景观变量沿城市化梯度影响蝴蝶组合的结构和组成

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摘要

How urbanization affects the distribution patterns of butterflies is still poorly known. Here we investigated the structure and composition of butterfly assemblages along an urbanization gradient within the most urbanized and densely populated region in France (Île-de-France). Using a method issued from artificial neural networks, i.e. self-organizing maps (SOMs), we showed the existence of four typical assemblages ranging from urban-tolerant species to urban-avoider species. We identified indicator species of these assemblages: the peacock butterfly (Inachis io) in urbanized areas, the swallowtail (Papilio machaon) in sites with intermediate human pressure, or the meadow brown (Maniola jurtina), the small heath (Coenonympha pamphilus) and the gatekeeper (Pyronia tithonus) in meadows around Paris. A discriminant analysis showed that the four assemblages were mainly segregated by landscape elements, both by structural variables (habitat type, proportion of rural areas and artificial urban areas, patch surface) and functional variables (distance to the nearest wood, artificial area and park). Artificial neural networks and SOMs coupled stepwise discriminant analysis proved to be promising tools that should be added to the toolbox of community and spatial ecologists.
机译:城市化如何影响蝴蝶的分布方式仍然知之甚少。在这里,我们研究了在法国(法兰西岛)最城市化和人口稠密的区域内,沿城市化梯度分布的蝴蝶组合的结构和组成。使用从人工神经网络发出的方法(即自组织图(SOM)),我们显示了从城市耐受物种到城市规避物种的四种典型组合的存在。我们确定了这些组合的指示物种:城市化地区的孔雀蝴蝶(Inachis io),中等压力的地点的燕尾(Papilio machaon)或草甸棕(Maniola jurtina),小荒地(Coenonympha pamphilus)和网守(Pyronia tithonus)在巴黎附近的草地上。判别分析表明,这四个集合主要是按照景观要素分类的,既按结构变量(人居类型,农村地区和人工城市地区的比例,斑块表面)又按功能变量(与最近的木材,人工区域和公园的距离)分开。人工神经网络和SOM结合逐步判别分析证明是有前途的工具,应将其添加到社区和空间生态学家的工具箱中。

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