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有限尺寸非均匀性对二维种群空间扩散的综合影响。

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions.

机构信息

Living Systems Institute, University of Exeter, Exeter, UK.

Physics and Astronomy, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.

出版信息

J R Soc Interface. 2021 Oct;18(183):20210579. doi: 10.1098/rsif.2021.0579. Epub 2021 Oct 20.

Abstract

The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here, we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speed-up, which we characterize as function of hotspot strength and density. Our findings emphasize the importance of taking the dimensionality of the environment into account.

摘要

一个种群向未占据栖息地扩张的动态主要研究了在空间中生长和扩散参数均匀或在一个维度上变化的情况。在这里,我们研究了有限大小的个体不均匀性及其在二维栖息地中随机放置时对前沿速度的集体影响。我们使用基于个体的模型来研究个体的扩散或生长分别减少到零(障碍物)或增加到背景之上(热点)的区域的前沿动力学。在前沿动力学仅由局部前沿速度决定的情况下,可以采用最小时间原理来预测前沿速度和形状。所得的解析解激发了一个基于事件的算法,说明了几个障碍物或热点的影响。我们最后通过数值求解 Eikonal 方程将最小时间原理应用于大的非均匀环境。障碍物导致减速,这主要由障碍物的数量密度和宽度决定,而与障碍物的精确形状无关。热点导致速度加快,我们将其特征描述为热点强度和密度的函数。我们的发现强调了考虑环境维度的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0652/8526172/8e5aee08d2b7/rsif20210579f01.jpg

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