Code Supplement for the article: Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology

This file contains all the code needed to replicate the analyses of McGlinn, D.J., X. Xiao, J. Kitizes, and E.P. White. submitted. Exploring the spatially explicit predictions of the Maximum Entropy Theory of Ecology. http://biorxiv.org/content/early/2014/03/30/003657 This file should be decompressed in what R recognizes as the home directory (the R code will need to be modified if you decide not to use your home directory). R v 2.12 or higher and python 2.6 are needed to run the code. The following python packages are required: matplotlib, mpmath, numpy, and scipy, and the following R packages are required: vegan, hash, RCurl and bigmemory. It is also possible to access this code via GitHub at the following addresses: The primary code repository is located at: https://github.com/weecology/mete-spatial Additional scripts needed to run the core METE DDR functions are located here: https://github.com/weecology/METE https://github.com/weecology/macroecotools After the files are downloaded (from GitHub) or decompressed (from the mete-spatial.zip file) navigate to the METE directory and run the following python command python setup.py install run the same command in the macroecotools directory. To download the publically available data and run the analysis navigate to the directory ~/mete-spatial and run the command: Rscript ddr_run_all.R This script will download two datasets, analyze them, and graph the results. The plots will appear in the directory ~/mete-spatial/figs/

本文件包含复现McGlinn、D.J.、X. Xiao、J. Kitizes与E.P. White已提交研究的全部分析代码。该研究题为《探索生态学最大熵理论（Maximum Entropy Theory of Ecology, METE）的空间显式预测》，预印本链接为http://biorxiv.org/content/early/2014/03/30/003657。
本文件需解压至R识别的主目录中，若不使用主目录，则需修改对应R代码。运行本代码需R 2.12及以上版本与Python 2.6；所需Python依赖包包括matplotlib、mpmath、numpy与scipy，所需R依赖包包括vegan、hash、RCurl及bigmemory。
用户亦可通过以下GitHub地址获取本代码：
主代码仓库地址：https://github.com/weecology/mete-spatial
运行核心METE DDR函数所需的额外脚本仓库地址如下：
https://github.com/weecology/METE
https://github.com/weecology/macroecotools
从GitHub下载代码或解压mete-spatial.zip文件后，进入METE目录，执行以下Python命令：python setup.py install，随后在macroecotools目录中执行相同命令。
如需下载公开可用数据集并完成分析，请进入~/mete-spatial目录，执行命令：Rscript ddr_run_all.R。该脚本将自动下载两个数据集，完成数据分析并绘制结果，生成的可视化图表将保存至~/mete-spatial/figs/目录中。