Occupancy models for citizen-science data

1. Large-scale citizen science projects, such as atlases of species distribution, are an important source of data for macroecological research, for understanding the effects of climate change and other drivers on biodiversity, and for more applied conservation tasks, such as early-warning systems for biodiversity loss.
2. However, citizen-science data are challenging to analyse because the observation process has to be taken into account. Typically, the observation process leads to heterogeneous and non-random sampling, false absences, false detections and spatial correlations in the data. Increasingly, occupancy models are being used to analyse atlas data.
3. We advocate a dual approach to strengthen inference from citizen science data for the questions the programme is intended to address. 1) the survey design should be chosen with a particular set of questions and associated analysis strategy in mind and 2) the statistical methods should be tailored not only to those questions, but...

1. 大规模公民科学项目（citizen science projects），例如物种分布图集，是宏观生态学研究的重要数据来源，可用于解析气候变化及其他驱动因子对生物多样性的影响，亦可服务于更具应用价值的保护工作，比如生物多样性丧失预警系统。
2. 然而，公民科学数据的分析颇具挑战性，因需将观测过程纳入考量范畴。通常而言，观测过程会导致数据出现异质性非随机抽样、假缺失记录、误检测结果以及空间相关性等问题。当前，占有率模型（occupancy models）正愈发广泛地被用于分析物种分布图集数据。
3. 我们主张采用双轨策略，以强化公民科学数据针对项目预设研究问题的统计推断效力。其一，应结合特定的研究问题与配套分析策略来选定调查设计方案；其二，统计方法不仅需适配上述研究问题，还……