A scalable algorithm for dispersing population
A scalable algorithm for dispersing population
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DOI：
10.1007/s10844-006-0030-z
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发表时间：
2007-08
影响因子：
3.4
通讯作者：
Sathish Govindarajan;M. Dietze;P. Agarwal;J. Clark
中科院分区：
文献类型：
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作者：
Sathish Govindarajan;M. Dietze;P. Agarwal;J. Clark
需要森林生态系统模型来了解气候和土地使用变化如何影响生物多样性。在本文中，我们描述了一个生态传播模型开发的特定情况下，预测种子传播的树木景观中使用的森林模拟模型。我们提出了有效的近似算法计算种子扩散。这些算法使我们能够长时间模拟大型景观。我们还提出了实验结果，（1）量化的固有不确定性的扩散模型和（2）描述的近似误差的变化作为近似参数的函数。基于这些实验，我们提供了选择正确的近似参数的指导方针，对于一个给定的模型模拟。
Models of forest ecosystems are needed to understand how climate and land-use change can impact biodiversity. In this paper we describe an ecological dispersal model developed for the specific case of predicting seed dispersal by trees on a landscape for use in a forest simulation model. We present efficient approximation algorithms for computing seed dispersal. These algorithms allow us to simulate large landscapes for long periods of time. We also present experimental results that (1) quantify the inherent uncertainty in the dispersal model and (2) describe the variation of the approximation error as a function of the approximation parameters. Based on these experiments, we provide guidelines for choosing the right approximation parameters, for a given model simulation.