Plotting receiver operating characteristic and precision-recall curves from presence and background data

  The receiver operating characteristic (ROC) and precision-recall (PR) plots have been widely used to evaluate the performances of species distribution models. Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence-only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results. In this study we propose a new approach to calibrate the ROC/PR curves from presence and background data with user-provided information on a constant c, namely PB approach. Here c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB-based ROC/PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB-base...受试者工作特征（Receiver Operating Characteristic, ROC）曲线与精确率-召回率（Precision-Recall, PR）曲线已被广泛用于物种分布模型（Species Distribution Models）的性能评估。绘制ROC/PR曲线需要同时涵盖存在与缺失数据的传统测试集（即PA法），但实际研究中往往难以获取物种缺失数据。若仅采用仅存在数据，并将背景数据视作伪缺失数据以绘制ROC/PR曲线（即PO法），则可能得到误导性结果。本研究提出一种新方法，可基于存在数据与背景数据，结合用户提供的常数c的相关信息来校准ROC/PR曲线（即PB法）。其中，常数c定义为物种出现被检测（标记）的概率；若已拥有具备优良分辨能力的模型，还可通过基于PB法的ROC/PR曲线推导出常数c的估计值。本研究使用5种虚拟物种与一组真实航空影像，对所提出的PB法的有效性进行了测试。