Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions
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DOI：
10.1111/ddi.13271
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发表时间：
2021-05-07
影响因子：
4.6
通讯作者：
Fink, Daniel
中科院分区：
文献类型：
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作者：
Johnston, Alison;Hochachka, Wesley M.;Fink, Daniel
由公众收集的生态数据对于解决广泛的生态研究和保护规划是有价值的，并且现有数据的范围和数量都在迅速增加。然而，来自eBird或其他有志愿者观察员的大型项目的数据通常会带来一些挑战，这些挑战可能会阻碍可靠的生态推断。这些挑战包括空间偏差，努力的变化和物种报告偏差。创新我们使用社区科学或公民科学（CS）项目eBird的数据来估计物种分布的例子。我们估计两个广泛使用的物种分布的度量：相遇率和占用概率。对于每一个指标，我们都严格评估了数据处理步骤的影响，这些步骤会降低或细化分析中使用的数据。CS数据密度在地球仪各地变化很大，所以我们也测试是否在模型性能的差异是强大的sample sizes.Main结论模型性能提高时，数据处理和分析方法解决了CS数据所带来的挑战，但是，改善的程度随物种和数据密度。我们在模型性能中观察到的最大收益是通过以下方式实现的：1）使用完整的检查表（观察员报告他们检测和识别的所有物种，允许推断未检测）和2）使用描述每个检查表的努力和可检测性变化的协变量。占领模式在缺乏完整清单的情况下更为稳健。随着样本量的增加，模型性能随着数据细化的改善更加明显。总的来说，我们发现每种改进的价值因情况而异，我们鼓励研究人员评估其他情况下的好处。这些方法将使研究人员能够更有效地利用CS数据中存在的大量生态知识进行保护和基础研究。
Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large-scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias.Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size.Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non-detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.