Fort Drum, NY Acoustic Data and R code

With the declines in abundance and changing distribution of white-nose syndrome–affected bat species, increased reliance on acoustic monitoring is now the new “normal.” As such, the ability to accurately identify individual bat species with acoustic identification programs has become increasingly important. We assessed rates of disagreement between the three U.S. Fish and Wildlife Service–approved acoustic identification software programs (Kaleidoscope Pro 4.2.0, Echoclass 3.1, and Bat Call Identification 2.7d) and manual visual identification using acoustic data collected during summers from 2003 to 2017 at Fort Drum, New York. We assessed the percentage of agreement between programs through pairwise comparisons on a total nightly count level, individual file level (e.g., individual echolocation pass call file), and grouped maximum likelihood estimate level (e.g., probability values that a species is misclassified as present when in fact it is absent) using preplanned contrasts, Akaike Information Criterion, and annual confusion matrices. Interprogram agreement on an individual file level was low, as measured by Cohen's Kappa (0.2–0.6). However, site-night level pairwise comparative analysis indicated that program agreement was higher (40–90%) using single season occupancy metrics. In comparing analytical outcomes of our different datasets (i.e., how comparable programs and visual identification are regarding the relationship between environmental conditions and bat activity), we determined high levels of congruency in the relative rankings of the model as well as the relative level of support for each individual model. This indicated that among individual software packages, when analyzing bat calls, there was consistent ecological inference beyond the file-by-file level at the scales used by managers. Depending on objectives, we believe our results can help users choose automated software and maximum likelihood estimate thresholds more appropriate for their needs and allow for better cross-comparison of studies using different automated acoustic software. A list of the files in the dataset, including a brief description of the types of files can be found in the 'Fort Drum, NY Acoustic Data and R code Text File.txt'

随着白鼻病影响的蝙蝠物种丰度下降和分布变化，对声学监测的依赖性不断增强，现已成为一种新的常态。因此，利用声学识别程序准确识别个体蝙蝠物种的能力变得愈发重要。本研究评估了美国鱼类和野生动物管理局（U.S. Fish and Wildlife Service）批准的三个声学识别软件程序（Kaleidoscope Pro 4.2.0、Echoclass 3.1和Bat Call Identification 2.7d）之间的不一致率，以及与人工视觉识别在2003年至2017年夏季收集于纽约州德拉姆堡的声学数据相比较。通过成对比较，在总夜间计数水平、单个文件水平（例如，单个回声定位飞行呼叫文件）以及分组最大似然估计水平（例如，当物种实际上不存在时被错误分类为存在的概率值）上，我们评估了程序之间的一致性百分比，并使用预先设计的对比、赤池信息量准则和年度混淆矩阵进行评估。在单个文件水平上，程序间的一致性较低，通过Cohen's Kappa（0.2–0.6）衡量。然而，在站点-夜间水平上的成对比较分析表明，使用单季节占用度指标时，程序间的一致性更高（40–90%）。在比较不同数据集的分析结果（即，不同程序和视觉识别在环境条件与蝙蝠活动之间的关系上的可比性）时，我们发现模型相对排名以及每个模型相对支持水平的契合度很高。这表明，在分析蝙蝠叫声时，单个软件包在管理层使用的尺度上，不仅限于文件层面，存在一致的生态推断。根据目标不同，我们相信我们的研究结果可以帮助用户选择更符合其需求的自动化软件和最大似然估计阈值，并允许更好地比较使用不同自动化声学软件的研究。数据集中文件的列表，包括文件类型的简要描述，可在'Fort Drum, NY Acoustic Data and R code Text File.txt'中找到。