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Journal Article

A Scalable Algorithm for Dispersing Population

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Govindarajan,  Sathish
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Citation

Govindarajan, S., Dietze, M. C., Agarwal, P. K., & Clark, J. S. (2007). A Scalable Algorithm for Dispersing Population. Journal of Intelligent Information Systems, 29(1), 39-61. doi:10.1007/s10844-006-0030-z.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1E35-7
Abstract
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.