Scher C Lane, Clark James S
Nicholas School of the Environment, Duke University, Durham, North Carolina, USA.
Department of Statistical Science, Duke University, Durham, North Carolina, USA.
Ecol Appl. 2023 Apr;33(3):e2815. doi: 10.1002/eap.2815. Epub 2023 Feb 22.
Datasets that monitor biodiversity capture information differently depending on their design, which influences observer behavior and can lead to biases across observations and species. Combining different datasets can improve our ability to identify and understand threats to biodiversity, but this requires an understanding of the observation bias in each. Two datasets widely used to monitor bird populations exemplify these general concerns: eBird is a citizen science project with high spatiotemporal resolution but variation in distribution, effort, and observers, whereas the Breeding Bird Survey (BBS) is a structured survey of specific locations over time. Analyses using these two datasets can identify contradictory population trends. To understand these discrepancies and facilitate data fusion, we quantify species-level reporting differences across eBird and the BBS in three regions across the United States by jointly modeling bird abundances using data from both datasets. First, we fit a joint Species Distribution Model that accounts for environmental conditions and effort to identify reporting differences across the datasets. We then examine how these differences in reporting are related to species traits. Finally, we analyze species reported to one dataset but not the other and determine whether traits differ between reported and unreported species. We find that most species are reported more in the BBS than eBird. Specifically, we find that compared to eBird, BBS observers tend to report higher counts of common species and species that are usually detected by sound. We also find that species associated with water are reported less in the BBS. Species typically identified by sound are reported more at sunrise than later in the morning. Our results quantify reporting differences in eBird and the BBS to enhance our understanding of how each captures information and how they should be used. The reporting rates we identify can also be incorporated into observation models through detectability or effort to improve analyses across species and datasets. The method demonstrated here can be used to compare reporting rates across any two or more datasets to examine biases.
监测生物多样性的数据集根据其设计不同而以不同方式获取信息，这会影响观察者行为，并可能导致观察结果和物种间出现偏差。整合不同的数据集可以提高我们识别和理解生物多样性威胁的能力，但这需要了解每个数据集中的观察偏差。两个广泛用于监测鸟类种群的数据集体现了这些普遍问题：eBird是一个公民科学项目，具有高时空分辨率，但在分布、调查力度和观察者方面存在差异；而繁殖鸟类调查（BBS）是对特定地点随时间进行的结构化调查。使用这两个数据集进行的分析可能会识别出相互矛盾的种群趋势。为了理解这些差异并促进数据融合，我们通过联合使用两个数据集的数据对鸟类丰度进行建模，来量化美国三个地区eBird和BBS之间物种水平的报告差异。首先，我们拟合一个联合物种分布模型，该模型考虑环境条件和调查力度，以识别数据集中的报告差异。然后，我们研究这些报告差异如何与物种特征相关。最后，我们分析只在一个数据集中报告而未在另一个数据集中报告的物种，并确定报告物种和未报告物种之间的特征是否存在差异。我们发现，大多数物种在BBS中的报告数量比在eBird中更多。具体而言，我们发现与eBird相比，BBS的观察者倾向于报告更多常见物种以及通常通过声音检测到的物种的数量。我们还发现，与水相关的物种在BBS中的报告较少。通常通过声音识别的物种在日出时的报告数量比上午晚些时候更多。我们的结果量化了eBird和BBS中的报告差异，以增进我们对每个数据集如何获取信息以及应如何使用它们的理解。我们识别出的报告率还可以通过可检测性或调查力度纳入观察模型，以改进跨物种和数据集的分析。这里展示的方法可用于比较任何两个或更多数据集的报告率，以检查偏差。
