Opportunistic and unstructured observations of biodiversity crowdsourced from volunteers, community, and citizen scientists make up an increasingly large proportion of our global biodiversity knowledge. This incredible wealth of information exists in real time at both high resolutions and large extents of space, time, and taxonomy, thus holding huge potential to fill gaps in global biodiversity monitoring coverage in a cost-effective way. Yet, the full potential of these data to provide essential indicators of biodiversity change for both research and management remains mostly unrealized, in large part due to the prevailing perception that the lack of standardization presents an unsurmountable barrier. In this paper, we provide an overview of the main challenges of working with unstructured community-contributed data and synthesize the four fundamental approaches to overcome these challenges and extract useful inferences of biodiversity change, namely: 1) reverse-engineering survey structure; 2) borrowing strength across taxa; 3) modeling the observation process, and; 4) integrating standardized data sources. To illustrate each of these approaches, we provide examples comparing community-contributed observations crowdsourced via iNaturalist with long-term standardized monitoring surveys for a subset of rocky intertidal organisms on the California coast from 2010 to 2019. We conclude by highlighting ways forward for the successful integration of unstructured community-contributed observations within the global ecosystem of biodiversity change monitoring tools. Our ultimate goal is to update the prevailing perception among researchers and practitioners that unstructured community-contributed observations of biodiversity are too noisy to use, and help establish this data stream as a key tool for research and management.

中文翻译：

---

从非结构化的社区贡献数据中推导出生物多样性变化的指标

来自志愿者、社区和公民科学家的对生物多样性众包的机会主义和非结构化观察占我们全球生物多样性知识的越来越大的比例。这种难以置信的丰富信息以高分辨率和大范围的空间、时间和分类实时存在，因此具有以具有成本效益的方式填补全球生物多样性监测覆盖空白的巨大潜力。然而，这些数据为研究和管理提供生物多样性变化的基本指标的全部潜力仍未实现，这在很大程度上是由于普遍认为缺乏标准化是不可逾越的障碍。在本文中，我们概述了使用非结构化社区贡献数据的主要挑战，并综合了四种基本方法来克服这些挑战并提取生物多样性变化的有用推论，即：1）逆向工程调查结构；2）跨类群的借贷实力；3) 对观测过程进行建模，以及；4) 整合标准化数据源。为了说明这些方法中的每一种，我们提供了示例，将通过 iNaturalist 众包的社区贡献观察结果与 2010 年至 2019 年加利福尼亚海岸岩石潮间带生物子集的长期标准化监测调查进行比较。在生物多样性变化监测工具的全球生态系统中整合非结构化社区贡献的观察结果。