Department of Mathematical and Statistical Sciences at the University of Alberta (UofA), Edmonton, Canada T6G 2R3.
Mathematical and Statistical Sciences and Biological Sciences at UofA, University of Alberta (UofA), Edmonton, Canada T6G 2R3.
J R Soc Interface. 2020 Sep;17(170):20200434. doi: 10.1098/rsif.2020.0434. Epub 2020 Sep 30.
When building models to explain the dispersal patterns of organisms, ecologists often use an isotropic redistribution kernel to represent the distribution of movement distances based on phenomenological observations or biological considerations of the underlying physical movement mechanism. The Gaussian, two-dimensional (2D) Laplace and Bessel kernels are common choices for 2D space. All three are special (or limiting) cases of a kernel family, the Whittle-Matérn-Yasuda (WMY), first derived by Yasuda from an assumption of 2D Fickian diffusion with gamma-distributed settling times. We provide a novel derivation of this kernel family, using the simpler assumption of constant settling hazard, by means of a non-Fickian 2D diffusion equation representing movements through heterogeneous 2D media having a fractal structure. Our derivation reveals connections among a number of established redistribution kernels, unifying them under a single, flexible modelling framework. We demonstrate improvements in predictive performance in an established model for the spread of the mountain pine beetle upon replacing the Gaussian kernel by the Whittle-Matérn-Yasuda, and report similar results for a novel approximation, the product-Whittle-Matérn-Yasuda, that substantially speeds computations in applications to large datasets.
在构建解释生物扩散模式的模型时,生态学家通常使用各向同性再分布核来表示基于现象观察或潜在物理运动机制的生物学考虑的运动距离分布。在二维空间中,高斯、二维(2D)拉普拉斯和贝塞尔核是常见的选择。这三个核都是核族的特例(或极限情况),即 Whittle-Matérn-Yasuda(WMY)核,由 Yasuda 从具有伽马分布沉降时间的二维 Fickian 扩散的假设中推导出。我们通过代表通过具有分形结构的异质二维介质的非 Fickian 二维扩散方程,使用更简单的恒定沉降危害假设,提供了这种核族的新推导。我们的推导揭示了许多已建立的再分布核之间的联系,将它们统一在一个单一的、灵活的建模框架下。我们通过用 Whittle-Matérn-Yasuda 替换高斯核,在已建立的山松甲虫扩散模型中证明了预测性能的改进,并报告了类似的结果,即乘积 Whittle-Matérn-Yasuda,它在应用于大数据集的计算时大大加快了计算速度。