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预测哺乳动物的扩散距离:基于特征的方法。

Predicting dispersal distance in mammals: a trait-based approach.

机构信息

Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, SL5 7PY, UK.

出版信息

J Anim Ecol. 2013 Jan;82(1):211-21. doi: 10.1111/j.1365-2656.2012.02030.x. Epub 2012 Aug 23.

Abstract

Dispersal is one of the principal mechanisms influencing ecological and evolutionary processes but quantitative empirical data are unfortunately scarce. As dispersal is likely to influence population responses to climate change, whether by adaptation or by migration, there is an urgent need to obtain estimates of dispersal distance. Cross-species correlative approaches identifying predictors of dispersal distance can provide much-needed insights into this data-scarce area. Here, we describe the compilation of a new data set of natal dispersal distances and use it to test life-history predictors of dispersal distance in mammals and examine the strength of the phylogenetic signal in dispersal distance. We find that both maximum and median dispersal distances have strong phylogenetic signals. No single model performs best in describing either maximum or median dispersal distances when phylogeny is taken into account but many models show high explanatory power, suggesting that dispersal distance per generation can be estimated for mammals with comparatively little data availability. Home range area, geographic range size and body mass are identified as the most important terms across models. Cross-validation of models supports the ability of these variables to predict dispersal distances, suggesting that models may be extended to species where dispersal distance is unknown.

摘要

扩散是影响生态和进化过程的主要机制之一,但遗憾的是,定量的实证数据非常匮乏。由于扩散可能会通过适应或迁移来影响种群对气候变化的反应,因此迫切需要获得扩散距离的估计值。识别扩散距离预测因子的跨物种相关方法可以为这一数据匮乏的领域提供急需的见解。在这里,我们描述了一个新的出生地扩散距离数据集的编制,并利用它来测试哺乳动物扩散距离的生活史预测因子,并检查扩散距离中的系统发育信号强度。我们发现,最大和中位数扩散距离都具有很强的系统发育信号。当考虑到系统发育时,没有一个单一的模型在描述最大或中位数扩散距离时表现最好,但许多模型显示出很高的解释能力,这表明在数据可用性相对较少的情况下,可以估计每一代哺乳动物的扩散距离。活动范围面积、地理范围大小和体重被确定为模型中最重要的术语。模型的交叉验证支持这些变量预测扩散距离的能力,这表明可以将模型扩展到扩散距离未知的物种。

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