Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt
Opportunities for improved distribution modelling practice via a strict maximum likelihood interpretation of MaxEnt
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DOI：
10.1111/ecog.00565
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发表时间：
2015-02-01
期刊：
影响因子：
5.9
通讯作者：
Bakkestuen, Vegar
中科院分区：
文献类型：
--
作者：
Halvorsen, Rune;Mazzoni, Sabrina;Bakkestuen, Vegar
在MaxEnt软件中实现的最大熵（MaxEnt）建模已迅速成为最流行的分布建模方法之一。最初，MaxEnt被描述为一种机器学习方法。最近，它已经从贝叶斯估计的原理来解释。MaxEnt为用户提供了许多选项（方法的变体）和设置（参数调优）。接受Maxent软件默认选项和设置的普遍做法已经建立起来，很可能是因为生态学家对机器学习和贝叶斯统计概念缺乏熟悉，以及在Maxent中容易获得默认模型。然而，在许多情况下，这些默认值已被证明是次优的，并且一再要求探索替代方案。在本文中，我们从严格的最大似然原理推导出MaxEnt，并指出MaxEnt与标准建模工具（如广义线性模型（GLM））之间的相似之处。此外，我们还描述了MaxEnt的新派生所带来的几个新选项，这可能会改进MaxEnt的实践。其中最重要的是通过子集选择方法来选择变量的选项，而不是(1)-正则化方法，这是目前Maxent软件默认的。其他新选项包括：合并解释变量的新转换和转换过程的用户控制；改进的变量贡献度量和变量划分选项；改进了输出预测格式。挪威东南部的一个植物物种Scorzonera humilis的数据集举例说明了新的选项，该数据集通过标准的MaxEnt程序在先前发表的一篇论文中进行了分析。我们建议对提议的备选方案和默认程序及其变体进行彻底的比较。
Maximum entropy (MaxEnt) modelling, as implemented in the Maxent software, has rapidly become one of the most popular methods for distribution modelling. Originally, MaxEnt was described as a machine-learning method. More recently, it has been explained from principles of Bayesian estimation. MaxEnt offers numerous options (variants of the method) and settings (tuning of parameters) to the users. A widespread practice of accepting the Maxent software's default options and settings has been established, most likely because of ecologists' lack of familiarity with machine-learning and Bayesian statistical concepts and the ease by which the default models are obtained in Maxent. However, these defaults have been shown, in many cases, to be suboptimal and exploration of alternatives has repeatedly been called for. In this paper, we derive MaxEnt from strict maximum likelihood principles, and point out parallels between MaxEnt and standard modelling tools like generalised linear models (GLM). Furthermore, we describe several new options opened by this new derivation of MaxEnt, which may improve MaxEnt practice. The most important of these is the option for selecting variables by subset selection methods instead of the (1)-regularisation method, which currently is the Maxent software default. Other new options include: incorporation of new transformations of explanatory variables and user control of the transformation process; improved variable contribution measures and options for variation partitioning; and improved output prediction formats. The new options are exemplified for a data set for the plant species Scorzonera humilis in SE Norway, which was analysed by the standard MaxEnt procedure in a previously published paper. We recommend that thorough comparisons between the proposed alternative options and default procedures and variants thereof be carried out.