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河流密度和景观粗糙度是语言多样性的普遍决定因素。

River density and landscape roughness are universal determinants of linguistic diversity.

机构信息

Centro de Astrobiología, INTA-CSIC, , Ctra. de Ajalvir km 4, Madrid, Torrejón de Ardoz 28850, Spain.

出版信息

Proc Biol Sci. 2014 Apr 16;281(1784):20133029. doi: 10.1098/rspb.2013.3029. Print 2014 Jun 7.

Abstract

Global linguistic diversity (LD) displays highly heterogeneous distribution patterns. Though the origin of the latter is not yet fully understood, remarkable parallelisms with biodiversity distribution suggest that environmental variables should play an essential role in their emergence. In an effort to construct a broad framework to explain world LD and to systematize the available data, we have investigated the significance of 14 variables: landscape roughness, altitude, river density, distance to lakes, seasonal maximum, average and minimum temperature, precipitation and vegetation, and population density. Landscape roughness and river density are the only two variables that universally affect LD. Overall, the considered set accounts for up to 80% of African LD, a figure that decreases for the joint Asia, Australia and the Pacific (69%), Europe (56%) and the Americas (53%). Differences among those regions can be traced down to a few variables that permit an interpretation of their current states of LD. Our processed datasets can be applied to the analysis of correlations in other similar heterogeneous patterns with a broad spatial distribution, the clearest example being biological diversity. The statistical method we have used can be understood as a tool for cross-comparison among geographical regions, including the prediction of spatial diversity in alternative scenarios or in changing environments.

摘要

全球语言多样性(LD)呈现出高度异质的分布模式。尽管后者的起源尚未完全理解,但与生物多样性分布的显著相似性表明,环境变量应该在其出现中发挥重要作用。为了构建一个广泛的框架来解释世界 LD 并对现有数据进行系统化,我们研究了 14 个变量的意义:景观粗糙度、海拔、河流密度、湖泊距离、季节性最高、平均和最低温度、降水和植被以及人口密度。景观粗糙度和河流密度是唯一普遍影响 LD 的两个变量。总体而言,所考虑的变量集可解释高达 80%的非洲 LD,而对于亚洲、澳大利亚和太平洋(69%)、欧洲(56%)和美洲(53%)的联合 LD,这一比例会降低。这些地区之间的差异可以追溯到少数几个变量,这些变量可以解释它们当前的 LD 状态。我们处理后的数据集可应用于具有广泛空间分布的其他类似异质模式的相关性分析,最明显的例子是生物多样性。我们使用的统计方法可以理解为地理区域之间的交叉比较工具,包括在替代情景或变化环境中预测空间多样性。

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