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Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations

机译：站点占用分布模型可校正机会主义观察得出的人口趋势估计

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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 detectablity 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.
机译：物种评估必须经常来自志愿者（即公民科学家）的机会性观察。解释得出的数据以估计人口趋势的过程面临许多问题，包括将真实的人口趋势与观测工作的差异区分开来。我们从机会观察的动物或花卉数据库中估算占用趋势（物种分布）时，设计了一种校正工作量年度变化的方法。首先，对于所有被调查站点，都会生成检测历史记录（即，检测到未检测到的记录的字符串）。季节内重复调查可提供有关占用站点可检测性的信息。可检测性直接代表观察工作;因此，估计可检测性意味着对观察工作进行校正。第二，将场所占用模型直接应用于检测历史数据集（即不按场所和年份进行汇总）以估计可检测性和物种分布（占用率，即发生物种的场所的真实比例）。场地占用模型还提供了分布变化成分（即定居和灭绝率）的无偏估计量。我们用瑞士一个大型公民科学项目的数据说明了我们的方法，该项目中现场鸟类学家记录了机会主义观察。我们分析了四种物种的数据：分布广泛的翠鸟（Alcedo atthis。）和Sparrowhawk（Accipiter nisus。）以及稀少的鹅口疮（Monticola saxatilis。）和Wallcreeper（Tichodroma muraria。）。我们的方法要求记录所有观察到的物种。可检测性<1，并且多年来变化。模拟显示出一定的鲁棒性，但我们主张记录完整的物种列表（清单），而不是记录单个物种的单独记录。观察工作量的表示及其对可检测性的影响为从随机观察中提取趋势信息时遇到的工作量差异问题提供了解决方案。我们希望我们的方法可广泛应用于全球生物多样性监测和物种分布建模。

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