What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements
What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements
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DOI：
10.1111/ddi.12477
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发表时间：
2016-11-01
影响因子：
4.6
通讯作者：
Tottrup, Anders P.
中科院分区：
文献类型：
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作者：
Geldmann, Jonas;Heilmann-Clausen, Jacob;Tottrup, Anders P.
目的了解土地覆盖和人类基础设施的背景空间数据的整合如何有助于减少采样工作中的空间偏差，并提高基于公民科学的物种记录计划的利用率。通过比较四个不同的公民科学项目，我们探讨了如何抽样设计的复杂性影响这些空间biases.Location丹麦，Europe.Methods的作用，我们使用了点过程模型来估计土地覆盖和人类基础设施的影响，从四个不同的公民科学物种记录计划的观测强度。然后，我们用这些结果来预测的不足和过度采样以及相对的生物多样性的“热点”和“沙漠”，占常见的空间偏差引入非结构化抽样designs.Results我们证明，解释力的空间偏差，如基础设施和人口密度增加的复杂性的抽样方案下降。尽管在农业景观的绝对采样努力低，这些地区仍然出现过采样观察到的物种丰富度相比。相反，森林和草地出现采样不足，尽管较高的绝对采样努力。我们还提出了一种新的和有效的分析方法来解决空间偏差的非结构化的抽样方案和一种新的方式来解决这种偏差，当更结构化的抽样是不是一个选项。主要结论我们表明，公民科学数据集，它依赖于未经训练的业余爱好者，更容易受到基础设施和人口密度的空间偏差。因此，在设计大规模参与项目的目标和议定书时应考虑到这一点。我们的研究结果表明，在上下文数据可用的情况下，对个体观察的强度进行建模可以帮助理解和量化空间偏差如何影响观察到的生物模式。
Aim To understand how the integration of contextual spatial data on land cover and human infrastructure can help reduce spatial bias in sampling effort, and improve the utilization of citizen science-based species recording schemes. By comparing four different citizen science projects, we explore how the sampling design's complexity affects the role of these spatial biases.Location Denmark, Europe.Methods We used a point process model to estimate the effect of land cover and human infrastructure on the intensity of observations from four different citizen science species recording schemes. We then use these results to predict areas of under-and oversampling as well as relative biodiversity 'hotspots' and 'deserts', accounting for common spatial biases introduced in unstructured sampling designs.Results We demonstrate that the explanatory power of spatial biases such as infrastructure and human population density increased as the complexity of the sampling schemes decreased. Despite a low absolute sampling effort in agricultural landscapes, these areas still appeared oversampled compared to the observed species richness. Conversely, forests and grassland appeared under-sampled despite higher absolute sampling efforts. We also present a novel and effective analytical approach to address spatial biases in unstructured sampling schemes and a new way to address such biases, when more structured sampling is not an option.Main conclusions We show that citizen science datasets, which rely on untrained amateurs, are more heavily prone to spatial biases from infrastructure and human population density. Objectives and protocols of mass-participating projects should thus be designed with this in mind. Our results suggest that, where contextual data is available, modelling the intensity of individual observation can help understand and quantify how spatial biases affect the observed biological patterns.