Vollering Julien, Halvorsen Rune, Mazzoni Sabrina
Department of Environmental Sciences Western Norway University of Applied Sciences Sogndal Norway.
Department of Research and Collections Natural History Museum University of Oslo Oslo Norway.
Ecol Evol. 2019 Sep 30;9(21):12051-12068. doi: 10.1002/ece3.5654. eCollection 2019 Nov.
The widely used "Maxent" software for modeling species distributions from presence-only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high-predictive performance but low-ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models-that is, models which are more complex but not necessarily predictively better-than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence-environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence-only data and logistic regression (GLM) for presence-absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.
广泛使用的“Maxent”软件用于仅基于出现数据建模物种分布(Phillips等人,《生态建模》,190,2006,231),往往会生成具有高预测性能但生态可解释性低的模型,并且Maxent在变量变换、模型拟合和模型选择方面的统计方法的影响仍未得到充分认识。特别是,已证明Maxent通过套索正则化进行模型选择的方法会给出不太简约的分布模型,即更复杂但不一定预测性更好的模型,而不是子集选择。在本文中,我们介绍了MIAmaxent R包,它提供了一种类似于Maxent的建模物种分布的统计方法,但使用子集选择而不是套索正则化。子集选择通常产生的更简单模型在生态上更具可解释性,使分布模型更基于生态理论是使用MIAmaxent的一个基本动机。为此,该包基于预期的出现 - 环境关系执行变量变换,并包含根据建模系统的知识探索数据和询问模型的工具。此外,MIAmaxent实现了两种不同类型的模型拟合:仅基于出现数据的最大熵拟合和基于存在 - 缺失数据的逻辑回归(广义线性模型)。与Maxent不同,MIAmaxent将变量变换、模型拟合和模型选择解耦,这便于进行方法比较,并在为给定的分布建模问题选择统计方法时给予建模者更大的灵活性。