New approaches for delineating n-dimensional hypervolumes
New approaches for delineating n-dimensional hypervolumes
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DOI：
10.1111/2041-210x.12865
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发表时间：
2018-02-01
影响因子：
6.6
通讯作者：
Kerkhoff, Andrew J.
中科院分区：
文献类型：
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作者：
Blonder, Benjamin;Morrow, Cecina Babich;Kerkhoff, Andrew J.
1.Hutchinson的n维超体积概念在当代生态学和进化生物学中有着广泛的应用。由于概念和计算问题，从采样数据估计超级体积一直是一个持续的挑战。我们提出了新的算法来描绘n维超体积内的边界和概率密度。这些方法产生平滑的边界，既可以更宽松地(高斯核密度估计)拟合数据，也可以更紧密地(通过支持向量机进行一次分类)拟合数据。此外，该算法可以接受丰度加权数据，所产生的超体积可以被给予概率解释并投影到地理空间。我们在描述数千种植物的功能特征和地理分布的大型数据集上演示了这些方法的特性。这些方法在HYPERVOLUME R包的版本>；=2.0.7中可用。这些新算法提供了：(I)更稳健的方法来描绘n维超体积的形状和密度；(Ii)在大型和高维数据集上的更有效的性能；以及(Iii)改进的功能多样性和环境生态位宽度的度量。
1. Hutchinson's n-dimensional hypervolume concept underlies many applications in contemporary ecology and evolutionary biology. Estimating hypervolumes from sampled data has been an ongoing challenge due to conceptual and computational issues.2. We present new algorithms for delineating the boundaries and probability density within n-dimensional hypervolumes. The methods produce smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one-classification via support vector machine). Further, the algorithms can accept abundance-weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space.3. We demonstrate the properties of these methods on a large dataset that characterises the functional traits and geographic distribution of thousands of plants. The methods are available in version >= 2.0.7 of the HYPERVOLUME R package.4. These new algorithms provide: (i) a more robust approach for delineating the shape and density of n-dimensional hypervolumes; (ii) more efficient performance on large and high-dimensional datasets; and (iii) improved measures of functional diversity and environmental niche breadth.