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Butterfly species richness in mainland Portugal: predictive models of geographic distribution patterns

机译:葡萄牙大陆蝴蝶物种丰富度:地理分布格局的预测模型

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A three-step protocol described elsewhere is used to obtain a map of butterfly species density in Portugal on a 50×50 km grid. First, all available faunistic information was compiled and analysed to explore the historic patterns of butterfly sampling in Portugal, and to determine which grid cells are sufficiently prospected to produce reliable estimates of species richness. Then, we relate the estimated species richness scores from these areas to a set of environmental and spatial variables by means of General Linear Models, obtaining a function to extrapolate of species density scores to the rest of Portugal. Finally, the model is validated, results explored and outliers identified and deleted. Any spatial autocorrelation remaining in the residuals is examined. Lastly, model parameters are recalculated in absence of deleted outliers, and the resulting function is used to predict species richness scores throughout mainland Portugal. A highly-predictive function based on some variables previously related to butterfly composition at macro-scale, such as number of sunny days per year, temperature or environmental heterogeneity, was obtained. However, in Portugal those variables are highly spatially structured along a steep latitudinal gradient, leading to difficulty in ascertaining if the latitudinal gradient detected by our analysis is due to macroecological or historic effects. Information on European and Iberian butterfly assemblages and causal processes are discussed in the light of the patterns observed. Then, previous information obtained on Portuguese scarabs is added to identify conservation areas, biogeographically important for both insect groups. Finally, the main drawbacks and advantages of this approach to mapping biodiversity for conservation are discussed briefly.
机译:使用在其他地方描述的三步协议在50×50 km的网格上获得葡萄牙蝴蝶物种密度的地图。首先,对所有可用的动物学信息进行汇编和分析,以探索葡萄牙蝶类采样的历史模式,并确定哪些网格单元可望足以产生可靠的物种丰富度估计值。然后,我们通过通用线性模型将这些地区的估计物种丰富度得分与一组环境和空间变量相关联,从而获得将物种密度得分外推到葡萄牙其他地区的功能。最后,对模型进行验证,探索结果并识别和删除异常值。检查残差中剩余的任何空间自相关。最后,在没有缺失异常值的情况下重新计算模型参数,并将所得函数用于预测整个葡萄牙大陆的物种丰富度得分。基于以前与宏观尺度上的蝴蝶组成相关的一些变量(例如每年的晴天数量,温度或环境异质性),获得了高度预测的函数。但是,在葡萄牙,这些变量沿陡峭的纬度梯度在空间上高度结构化,导致难以确定我们的分析所检测到的纬度梯度是由于宏观生态学还是历史作用所致。根据观察到的模式,讨论了有关欧洲和伊比利亚蝴蝶组合和因果过程的信息。然后,添加以前在葡萄牙圣甲虫上获得的信息来确定保护区,这对于两个昆虫群体在生物地理上都是重要的。最后,简要讨论了这种为保护生物多样性作图的方法的主要弊端和优点。

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