稀释
采样(信号处理)
生态位
生态学
利基
空间分析
计算机科学
空间生态学
R包
统计
数学
栖息地
生物
计算科学
滤波器(信号处理)
计算机视觉
作者
Matthew E. Aiello‐Lammens,Robert A. Boria,Aleksandar Radosavljević,Bruno Vilela,Robert P. Anderson
出处
期刊:Ecography
[Wiley]
日期:2015-02-06
卷期号:38 (5): 541-545
被引量:1235
摘要
Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.
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