Morales Narkis S, Fernández Ignacio C, Baca-González Victoria
Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia.
Fundación Ecomabi, Santiago, Región Metropolitana, Chile.
PeerJ. 2017 Mar 14;5:e3093. doi: 10.7717/peerj.3093. eCollection 2017.
Environmental niche modeling (ENM) is commonly used to develop probabilistic maps of species distribution. Among available ENM techniques, MaxEnt has become one of the most popular tools for modeling species distribution, with hundreds of peer-reviewed articles published each year. MaxEnt's popularity is mainly due to the use of a graphical interface and automatic parameter configuration capabilities. However, recent studies have shown that using the default automatic configuration may not be always appropriate because it can produce non-optimal models; particularly when dealing with a small number of species presence points. Thus, the recommendation is to evaluate the best potential combination of parameters (feature classes and regularization multiplier) to select the most appropriate model. In this work we reviewed 244 articles published between 2013 and 2015 to assess whether researchers are following recommendations to avoid using the default parameter configuration when dealing with small sample sizes, or if they are using MaxEnt as a "black box tool." Our results show that in only 16% of analyzed articles authors evaluated best feature classes, in 6.9% evaluated best regularization multipliers, and in a meager 3.7% evaluated simultaneously both parameters before producing the definitive distribution model. We analyzed 20 articles to quantify the potential differences in resulting outputs when using software default parameters instead of the alternative best model. Results from our analysis reveal important differences between the use of default parameters and the best model approach, especially in the total area identified as suitable for the assessed species and the specific areas that are identified as suitable by both modelling approaches. These results are worrying, because publications are potentially reporting over-complex or over-simplistic models that can undermine the applicability of their results. Of particular importance are studies used to inform policy making. Therefore, researchers, practitioners, reviewers and editors need to be very judicious when dealing with MaxEnt, particularly when the modelling process is based on small sample sizes.
环境生态位建模（ENM）通常用于绘制物种分布概率图。在现有的ENM技术中，最大熵模型（MaxEnt）已成为最受欢迎的物种分布建模工具之一，每年都有数百篇经过同行评审的文章发表。MaxEnt之所以受欢迎，主要是因为它使用了图形界面和自动参数配置功能。然而，最近的研究表明，使用默认的自动配置并不总是合适的，因为它可能会产生非最优模型；特别是在处理少量物种出现点时。因此，建议评估参数（特征类和正则化乘数）的最佳潜在组合，以选择最合适的模型。在这项工作中，我们回顾了2013年至2015年间发表的244篇文章，以评估研究人员在处理小样本量时是否遵循了避免使用默认参数配置的建议，或者他们是否将MaxEnt用作“黑箱工具”。我们的结果表明，在仅16%的分析文章中，作者评估了最佳特征类，6.9%的文章评估了最佳正则化乘数，而在最终生成分布模型之前，同时评估这两个参数的文章仅有3.7%。我们分析了20篇文章，以量化使用软件默认参数而非替代的最佳模型时，结果输出的潜在差异。我们的分析结果揭示了使用默认参数和最佳模型方法之间的重要差异，特别是在被确定为适合评估物种的总面积以及两种建模方法都确定为适合的特定区域方面。这些结果令人担忧，因为出版物可能报告的是过于复杂或过于简单的模型，这可能会削弱其结果的适用性。对于用于为政策制定提供信息的研究而言，这一点尤为重要。因此，研究人员、从业者、审稿人和编辑在使用MaxEnt时需要非常谨慎，尤其是当建模过程基于小样本量时。
