Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, California, United States of America.
Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, California, United States of America.
PLoS One. 2021 Feb 22;16(2):e0244040. doi: 10.1371/journal.pone.0244040. eCollection 2021.
Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Año Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was 'Median without the lowest two values', demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with imagery from drones, occupied aircraft, and/or remote cameras.
重复计数动物的数量可以揭示当地生态系统健康的变化，并为保护策略提供信息。无人机系统（UAS）也称为无人机，通常用于拍摄偏远地区的动物照片；但是，在图像中计数动物是一项繁琐的任务。众包可以大大减少进行这些普查所需的时间，但必须首先经过专家计数验证，以衡量误差的来源。我们的目标是评估公民科学计数的准确性和精密度，并为未来的公民科学项目提出建议。我们将来自加利福尼亚州亚诺纽纽岛（Año Nuevo Island）的无人机图像上传到一个经过策划的 Zooniverse 网站，该网站指示公民科学家对海豹和海狮进行计数。在 212 天的时间里，超过 1500 名志愿者对 90000 张照片中的动物进行了计数。我们量化了与几个描述性统计量相关的误差，以从 15 次重复计数中提取每张照片的单个公民科学计数，然后将得出的公民科学计数与专家计数进行比较。尽管比例误差相对较低（繁殖季节的海狮为 9%，海豹为 5%），并且随着重复采样而提高，但需要 12 名以上的志愿者才能减少误差，这是非常缓慢的，平均需要 6 周的时间才能从覆盖 25 英亩的单个无人机飞行中估算出动物数量，尽管进行了强有力的公众宣传。最佳算法是“去掉最低两个值的中位数”，这表明公民科学家往往低估了存在的动物数量。公民科学家准确地计数了成年海豹，但当夏季出现海狮时，准确性较低，并且可能与海豹混淆。我们强调了研究人员希望将公民科学与无人机、有人驾驶飞机和/或远程摄像机拍摄的图像相结合时，验证工作和精心设计项目的重要性。
