ABSTRACT Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numerous manuscripts on this subject, we frequently faced some recurrent mistakes: 1) presence-background modelling methods, such as Maxent or ENFA, are used as if they were pseudo-absence methods; 2) spatial autocorrelation is confused with clustering of species records; 3) environmental variables are used with a higher spatial resolution than species records; 4) correlations between variables are not taken into account; 5) machine-learning models are not replicated; 6) topographical variables are calculated from unprojected coordinate systems, and; 7) environmental variables are downscaled by resampling. Some of these mistakes correspond to student misunderstandings and are corrected before publication. However, other errors can be found in published papers. We explain here why these approaches are erroneous and we propose ways to improve them.

中文翻译：

---

生态位模型中的常见错误

摘要生态位模型（ENMs）是广泛使用的统计方法来估计各种类型的物种生态位。在讲授了几版 ENM 介绍性课程并查阅了大量关于该主题的手稿后，我们经常面临一些反复出现的错误：1）存在背景建模方法，例如 Maxent 或 ENFA，被当作伪不存在方法使用；2）空间自相关与物种记录的聚类混淆；3) 环境变量的使用空间分辨率高于物种记录；4) 不考虑变量之间的相关性；5）机器学习模型不被复制；6) 地形变量是根据未投影的坐标系计算的，并且；7) 环境变量通过重采样缩小规模。其中一些错误对应于学生的误解，并在出版前予以纠正。但是，在已发表的论文中可以发现其他错误。我们在这里解释了为什么这些方法是错误的，我们提出了改进它们的方法。