Species traits and observer behaviors that bias data assimilation and how to accommodate them.
Species traits and observer behaviors that bias data assimilation and how to accommodate them.
复制标题
影响数据同化的物种特征和观察者行为以及如何适应它们。
作者：
C. Scher;J. Clark
监测生物多样性的数据集根据其设计不同而捕获不同的信息，这会影响观察者的行为，并可能导致观察和物种之间的偏差。结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的观测偏差。广泛用于监测鸟类种群的两个数据集解决了这些普遍关注的问题：eBird是一个公民科学项目，具有高时空分辨率，但分布，努力和观察员的变化，而繁殖鸟类调查（BBS）是一个结构化的调查，随着时间的推移，特定的位置。使用这两个数据集的分析可以识别相互矛盾的人口趋势。为了理解这些差异并促进数据融合，我们通过使用来自两个数据集的数据联合建模鸟类丰度，量化了美国三个地区eBird和BBS之间的物种水平报告差异。首先，我们拟合了一个联合物种分布模型，该模型考虑了环境条件和识别数据集之间报告差异的努力。然后，我们研究这些差异的报告是如何与物种特征。最后，我们分析了一个数据集报告的物种，而不是另一个数据集，并确定报告和未报告的物种之间的性状是否不同。我们发现大多数物种在BBS上的报道比eBird多。具体来说，我们发现，与eBird相比，BBS观察员倾向于报告更高的常见物种和通常通过声音检测到的物种计数。我们还发现，与水有关的物种在BBS中报道较少。通常通过声音识别的物种在日出时比早上晚些时候报告得更多。我们的研究结果量化了eBird和BBS的报告差异，以提高我们对各自如何捕获信息以及如何使用它们的理解。我们确定的报告率也可以通过可检测性或努力改善跨物种和数据集的分析来纳入观察模型。这里演示的方法可用于比较任何两个或多个数据集的报告率，以检查偏倚。
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 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 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.