最大熵原理
野生动物
计算机科学
选择(遗传算法)
选型
熵(时间箭头)
采样(信号处理)
栖息地
数据挖掘
统计
计量经济学
机器学习
生态学
人工智能
数学
物理
生物
滤波器(信号处理)
量子力学
计算机视觉
出处
期刊:Entropy
[MDPI AG]
日期:2009-11-16
卷期号:11 (4): 854-866
被引量:661
摘要
Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.
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