Data from: Comparison of photo-matching algorithms commonly used for photographic capture-recapture studies

Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost-effectiveness. Recently, several computer-aided photo-matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state-of-the-art photo-matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo-matching algorithms; Wild-ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel-based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.

照相法重捕法（photographic capture–recapture）凭借非侵入性与成本效益优势，成为获取野生动物种群统计信息的重要工具。近年来，诸多计算机辅助照片匹配算法被研发出来，以更高效地在包含数千张图像的数据库中匹配个体独有图像。然而，此类算法的识别精度可能会严重偏倚种群生命率与种群规模的估算结果。因此，在涉及数千张图像的重捕研究中应用当前最先进的照片匹配算法前，明晰其性能与局限性至关重要。本研究针对四款照片匹配算法——Wild-ID、I3S Pattern+、APHIS与AmphIdent——展开性能对比，所用数据集为多组图像质量各异的两栖动物图像数据库。我们对每种算法的性能进行了量化测试，并评估了算法性能与数据库规模、数据库内匹配图像数量之间的相关性。研究结果显示，不同算法与图像数据库的性能差异显著：当仅评估排名前10的匹配结果时，识别率区间为22.6%至100%。我们还发现，识别率会随数据库规模扩大出现小幅下降，而数据库内匹配图像数量越多，识别率则可得到显著提升。本研究中，基于像素的AmphIdent算法相较其余三款算法展现出更优的识别性能。我们建议在使用算法匹配完整数据库前，务必对其性能进行审慎评估。通过选用适配的匹配算法，原本无法依靠人工逐张比对的大型数据库，可快速转化为精准的个体重捕记录，为稳健的种群统计估算提供必要支撑。