1. The area under the curve (AUC) of the receiving‐operating characteristic (or certain modifications of it) is almost universally used to assess the performance of species distribution models (SDMs), despite the well‐recognized problems encountered with this approach, mainly present when dealing with presence‐only data.
2. We present a probabilistic treatment of the presence‐only problem and derive a method to assess the performance of SDMs based on the analysis of an area‐presence plot and the SDM outputs represented in both geographic and environmental spaces.
3. We show how our method is useful to solve the two main tasks for which the AUC is used: assessing the performance of an SDM and comparing the performance of different SDMs. Our results build on previous work and constitute a rigorous method for assessing the performance of SDMs in relation to a random classifier.
4. We establish comparisons with two of the most popular approaches used to assess the performance of an SDM, the AUC and the Boyce index, and identified cases in which our method has advantages over these two approaches.
5. We suggest that the performance of an algorithm that classifies presence‐only data can be assessed by two factors: (a) the degree of non‐randomness of the classification at every step in the accumulation curve of presences, and (b) the amount of uninformative niche space used for the classification. The method we developed can be applied to any SDM output by using the R functions available at: https://github.com/LauraJim/SDM‐hyperTest.

中文翻译：

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将接收操作特征曲线下的区域留给后面：基于仅存在数据的物种分布建模应用程序的评估方法

1. 尽管这种方法遇到了公认的问题，但接收操作特性曲线下的面积（或对其的某些修改）几乎普遍用于评估物种分布模型（SDM）的性能。处理仅存在状态的数据时。
2. 我们提出了一种仅存在问题的概率处理方法，并基于对区域存在图和在地理和环境空间中表示的SDM输出的分析，得出了一种评估SDM性能的方法。
3. 我们展示了我们的方法对于解决使用AUC的两个主要任务如何有用：评估SDM的性能和比较不同SDM的性能。我们的结果以先前的工作为基础，并构成了一种评估SDM与随机分类器相关性能的严格方法。
4. 我们与用于评估SDM性能的两种最流行的方法（AUC和Boyce指数）建立了比较，并确定了我们的方法比这两种方法更具优势的情况。
5. 我们建议可以通过两个因素来评估对仅存在数据进行分类的算法的性能：（a）存在累积曲线中每一步的分类非随机程度，以及（b）用于分类的无信息的利基空间。通过使用以下网址提供的R函数，我们开发的方法可以应用于任何SDM输出：https：//github.com/LauraJim/SDM-hyperTest。