Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa.
Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa.
复制标题
DOI：
10.1371/journal.pone.0147796
复制
发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Ruete A
中科院分区：
文献类型：
--
作者：
Mair L;Ruete A
对开放式生物多样性数据库中的公民科学数据进行整理，使广泛的用户可以获得时间和空间上广泛的物种观测数据。这些数据是一种宝贵的资源，但也有固有的局限性，如有利于记录分布的抽样偏差，缺乏调查工作评估，以及缺乏对所有生物分布的覆盖。因此，任何应用公民科学数据的技术评估、监测方案或科学研究都应包括对其结果不确定性的评估。我们使用“无知”的分数，即在研究区域的采样偏差的空间显式指数，以进一步了解空间格局的观察行为的13个参考分类组。该数据基于2000年至2014年在瑞典进行的自愿观察。我们比较了六个地理变量（海拔，陡度，人口密度，原木人口密度，道路密度和人行道密度）对每组无知分数的影响。我们发现，在不同的地理变量解释无知分数的相对重要性的分类群体之间的实质性变化。在一般情况下，道路的访问和记录的人口密度始终是重要的变量解释抽样工作的偏差，表明在一个小规模的访问方便公民科学家自愿报告。此外，人口密度的小幅增加也会导致无知分数的大幅下降。然而，解释无知分数的地理变量的重要性的类群之间的变化表明，不同的类群遭受不同的空间偏差。我们建议保护主义者和研究人员应该使用无知分数来承认他们的分析和结论中的不确定性，因为它们可能同时包括许多难以解开的相关变量。
The collation of citizen science data in open-access biodiversity databases makes temporally and spatially extensive species’ observation data available to a wide range of users. Such data are an invaluable resource but contain inherent limitations, such as sampling bias in favour of recorder distribution, lack of survey effort assessment, and lack of coverage of the distribution of all organisms. Any technical assessment, monitoring program or scientific research applying citizen science data should therefore include an evaluation of the uncertainty of its results. We use ‘ignorance’ scores, i.e. spatially explicit indices of sampling bias across a study region, to further understand spatial patterns of observation behaviour for 13 reference taxonomic groups. The data is based on voluntary observations made in Sweden between 2000 and 2014. We compared the effect of six geographical variables (elevation, steepness, population density, log population density, road density and footpath density) on the ignorance scores of each group. We found substantial variation among taxonomic groups in the relative importance of different geographic variables for explaining ignorance scores. In general, road access and logged population density were consistently important variables explaining bias in sampling effort, indicating that access at a landscape-scale facilitates voluntary reporting by citizen scientists. Also, small increases in population density can produce a substantial reduction in ignorance score. However the between-taxa variation in the importance of geographic variables for explaining ignorance scores demonstrated that different taxa suffer from different spatial biases. We suggest that conservationists and researchers should use ignorance scores to acknowledge uncertainty in their analyses and conclusions, because they may simultaneously include many correlated variables that are difficult to disentangle.