Swiss Ornithological Institute, 6204 Sempach, Switzerland.
Conserv Biol. 2010 Oct;24(5):1388-97. doi: 10.1111/j.1523-1739.2010.01479.x.
Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectability means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.
物种评估通常需要根据志愿者（即公民科学家）的偶然观察结果得出。在解释这些数据以估计种群趋势时，存在许多问题，包括从观察工作的变化中区分真正的种群趋势。我们设计了一种方法，可在从偶然观察的动物区系或植物区系数据库中估算占有趋势（物种分布）时，纠正年度努力变化的影响。首先，对于所有调查的地点，都会生成检测历史记录（即检测-未检测记录的字符串）。在本季内重复调查提供了有关已占用地点可检测性的信息。可检测性直接代表观察工作；因此，估计可检测性意味着纠正观察工作。其次，直接将站点占有率模型应用于检测历史记录数据集（即，无需按站点和年份进行聚合），以估计可检测性和物种分布（占有率，即物种发生的实际站点比例）。站点占有率模型还提供了分布变化（即，定植和灭绝率）组成部分的无偏估计。我们使用瑞士一个大型公民科学项目的数据说明了我们的方法，在该项目中，野外鸟类学家记录了偶然观察结果。我们分析了收集到的四种物种的数据：广泛分布的翠鸟（Alcedo atthis）和雀鹰（Accipiter nisus）以及稀有的岩鸫（Monticola saxatilis）和墙百灵（Tichodroma muraria）。我们的方法要求记录所有观察到的物种。可检测性<1 且多年来有所变化。模拟表明具有一定的鲁棒性，但我们提倡记录完整的物种列表（清单），而不是记录单个物种的单个记录。观察工作的代表及其对可检测性的影响提供了一种解决方案，可以解决从偶然观察中提取趋势信息时遇到的努力差异问题。我们希望我们的方法在全球生物多样性监测和物种分布建模中具有广泛的适用性。
